diff --git a/.gitignore b/.gitignore
index 96cb0b2..8d278f2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,4 +132,9 @@ dmypy.json
 .github/templates/*
 
 # generated by rtx-examples
-temp.gif
\ No newline at end of file
+temp.gif
+
+*.vla
+*.mkv
+*.csv
+*.pdf
\ No newline at end of file
diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb
new file mode 100644
index 0000000..532322e
--- /dev/null
+++ b/benchmarks/Visualization.ipynb
@@ -0,0 +1,1048 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "  plt.tight_layout()  # Adjust layout to make room for the legend\n",
+      "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "  plt.tight_layout()  # Adjust layout to make room for the legend\n",
+      "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "  plt.tight_layout()  # Adjust layout to make room for the legend\n",
+      "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "  plt.tight_layout()  # Adjust layout to make room for the legend\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pUsqXLw9og4K6EQz5RVJSEkWLFmXVqlVpAhkAr7/+OoMGDVKeb9++3eh+bt68ydatW5Xnc+bMMfr3ulatWixduhQ3NzeLArxCCCGEyH8kmCEKhFdeeYUuXbqYXF+9enXeeecd5fn69evTtAkICOD48eOA9gP1kCFD0j2mi4sLo0ePVp5v27Ytw36OHj0aT0/PDNsZo9FomDNnDj/88AOgvfGaP3++wY2+vqSkJP7++2+l7VdffZVhVaFPPvlEmcJhzusxJivOY58+fZSh6//99x9RUVEm96cf7DB1bqwVEhLCzp07leczZszIcJqRLXTu3FlJsnjs2DGztvHy8mL48OEm15csWVIJKoD2/OmX1raEfiDkq6++yjCZ7HvvvacEuXbs2GGQjyGrdO7cmUaNGqXbZt26dUpfXnzxRQYMGGCybdGiRRk/frzyfPv27UoOm/xi1KhR6eYF0h/9ZCr3i/7vZf369enatavJ/ZUvX55hw4ZloqdCCCGEyE8kmCEKBFPDmk21uXjxInFxcQbrDxw4oDzu2rWrWTks9Kc7nDlzJsP26QVc0pOcnMzEiROVm0U3Nzf++OMPo3Pkdfz9/QkLCwOgWbNmFC9ePMPjlC5dmipVqgDaqSKZuSnLivNYqlQpJYlnQkKCyUCLv78/AQEBgPYms1OnTpZ03WxHjx4lOTkZ0H47b2pERmYEBASwadMmFi5cyNy5c5k5c6byb/bs2cr5vHbtmlk3/926dTOZw0CnR48eSl6RR48ecevWLYv7/ejRI65cuQJoR3zUrFkzw22cnJyoV68eoE2Weu3aNYuPa6n0bqJ1dME40FYSyeg93LFjRyU3RWJiIufOnbOqj7lNRnlAqlSpogSuIiIiiImJSdNGP/mwOflRevbsaWEvhRBCCJHfSM4MUSDobojS4+3tjYuLC3FxcaSkpHD16lWDyib6NyAnTpzg/v37Ge5T/xtsXRJHU7y8vDKVjC8+Pp7Ro0crQYLixYuzZMmSDKsL+fn5KY8fPnzIzJkzzTqebtSDRqPh4cOHFufNyKrz+MYbbyj5ATZu3Ggw0kZH/9vfHj164OTkZHa/LaF/bhs3bmyTfW7atInffvvNIDliepKSkoiOjs4wv4U51Xt05VJv3LgBwJUrV6hatapZ/dDRPydqtdrs95t+pZuHDx+aFQSxRka/NxqNRgmIgXnnr1ChQtSpU4dDhw4B2rwPtgxw5SR3d3fKli2bbhuVSkWRIkWUqWWxsbEGZbA1Go1SEhfg5ZdfzvC4FSpUoFixYspULiGEEEIUPBLMEAVCRh+2QfuBu0yZMsq3zuHh4QbrHz16pDw+ePCgxX1Ib+oDkOnpJcuXL1dGAZQtW5Zly5ZRuXLlDLfTfz1Xr141uJkwV2RkpMXbZNV5bNmyJeXLlyc4OJhLly5x5coVg6o0arXaYL5+ViX+BJQRL4DVSSs1Gg2TJk3Cx8fH4m1jY2MzDGaY87uha6cLZjz/u2EO/eseFBSkTHGyRGbeb5bK6PcwOjpaqcADKPkwMqLfLj/dgJsbzNRPqKx//iDtOTX3PVmmTJl8dS6FEEIIYRmZZiIKhMKFC1vcTr+cJmB0aLQldJVFTMkof4Ap+lMEIiMjlcokGbHFvP2MXpMxWXUe7ezs6Nu3r/L8+USgu3btUl5znTp1svQbfv33ji6HRWatW7fOIJDRsmVLvv32W7Zt28apU6e4ePGiEoy6evWqwU2zOdNMzP3d0H8dz/9umCOn3m+Wyuj38PnpZ9l1/nIrS0tGG/P8OTX3b6G1v1tCCCGEyNtkZIYoEOLj4w2GNafXTsfV1dVgnf5Ny8KFC9PNR5GdhgwZwrVr19i3bx9xcXG89957LF68OMMkhvqvZ9CgQUyZMiWru5rmuLY+j/369WPRokWkpKSwbds2JkyYoEwlyY7Enzr6753nb9Qs9ddffymPx44dy5gxY9Jtb+mNsv57Pj36r+P53w1z6F/3du3a8euvv1q8j9zg+Rvo+Ph4s26qrT1/z8uOZKjZ5fnzp1arzTqn5r53hRBCCJE/ycgMUSBklK8CtMP5Q0JClOe6cpQ6+tUozB39kB0KFSrEzz//TNu2bQGUgMbp06fT3U7/9Tx+/DhL+2jquLY+j6VLl1ZyEURGRrJ7925Am3fh1KlTgPbGyZwkj9bQT6YaFBSU6f08ePBAyZFRpEgR3n///XTbx8TEWDwVw5zfDdDmq9B5/nfDHDn1frM1d3d3gykT5uR8AQgODlYeGzt/+iOszBmBYu0Ip9zk+XOq/15Lj7nthBBCCJE/STBDFAj6yQdNuXbtmvKttr29fZppCHXr1lUenz171qb9s5ajo2OagMbIkSPTDWjov55z585lutympbL6PL755pvKY91ojI0bNyqv77XXXjNrlI419BPOnjhxItP70c8zUaVKFYMbPmPOnDlj8XU053cjKirKoIJJrVq1LDoGGCZ1vHLlitUjVnKKSqUy+NtgTmWS5ORkg5Kkxs6f/nvSnDwQ5uS4scUUkOygUqmoUaOG8vz8+fMZbhMUFJSp3C1CCCGEyD8kmCEKhK1bt2bYZvPmzcrjOnXqpBnmrAsUAOzevTvXfbtsaUCjYcOGFClSBNB+w7l3795s6WdWn8dWrVopCQSPHz/O3bt3DXJOZGXiT53mzZsr37TfuXNHqWJhKf2bUXOG1K9evdriY+zYsSPDkQDbtm1T2pQsWVIpz2uJChUqKBVQkpKS0uQ0yUv0SwVv3rw5wwCSr68vERERgLbcrLEKKPq5TvSrpRiTkJDAvn37MuynfrWe55Nu5jZNmjRRHpsqrazPnL/pQgghhMjfJJghCoSTJ0/y77//mlx/8+ZNg+oKxm5469atq5TZVKvVTJgwgcTERLOOn5iYmC2VGHQBjTZt2gDpBzQcHR0ZMmSI8vyrr74ymGaTkcwGIbL6PNrb2yuJQDUaDZ9//rkywqFq1ao0bNgwU/22ROnSpXn99deV59OnT8/U+fLy8lICGtevXycwMNBk23/++cesG9zn3bt3j2XLlplc//jxYxYtWqQ879evX6a/8R85cqTy+KeffrKogk5umtr1xhtvYGen/e/z0qVLrF271mTbqKgo5s2bpzzv2rWr0Qog+iNX9u3bl+6og/nz55s1ekO/1LP+KJ/cSD9575kzZ9i5c6fJtg8ePDDIJSOEEEKIgkmCGaJAKFSoEBMnTjQozalz7tw53n33XRISEgCoXr06PXv2NLqfqVOnKiM2jhw5wsCBA9MdEn379m0WLVpEu3btsm1qiqOjIwsWLDAroDFs2DCqV68OQEhICH379mXnzp0mkwuGh4ezdu1aevfuzZ9//pnpPmb1eezXr59ys6m/3+wYlaHz2WefKTeTwcHBvPnmmyZHaERFRbF27Vq+++47g+Wenp7KlJXU1FQ++ugjg+keuuV///03EyZMwN7e3uDbeHMUKlSI77//nuXLl6e57jdv3mTYsGFKqdkSJUowdOhQi/avr0ePHsqohtjYWAYMGMCaNWtMBrNiYmLYunUrgwYNYtasWZk+rq1VrFjRYDrTrFmz+Pvvv9Ocv7t37zJ8+HAlb4qbmxsffvih0X3WqVOHihUrAtrf2c8++yxN4C4+Pp5vv/2WP//8E0dHxwz76eXlpSReDQ4O5sKFC+a/yGxWrVo1unXrpjz/4osvjP69DggIYOjQoURHR5t1DoQQQgiRf9m8mklISAg7d+7k7NmzPHjwgKioKFJSUvD19TVoFx8fryREK1q0KCVLlrR1V4RQfP7553zzzTd89tlnLFiwgLp16+Lg4MD169cN5rK7uLgwd+5ckx+Sa9Sowf/93//xySefEB8fz/nz53njjTeoWLEitWrVomjRoiQmJhIWFsbVq1ctGulgS7qAxtixY9m/f78S0FiyZAmvvPKK0s7V1ZVff/2VoUOHEhQURGhoKB9//DHFihWjXr16lChRAo1GQ2RkJDdu3ODu3bvKDZv+UHtLZfV5LFu2LC1btuTAgQPKskKFCtGrV69M99lSZcuW5aeffmL06NHExcURFBTEiBEjKF++PHXq1KFo0aLExcVx584dAgICSEpKon379mn2M27cOIYPH05qaiqXL1+mR48e1K9fnwoVKhAXF8fp06eVUQuffPIJ69atM0g2mRHd78Y333zDX3/9RcOGDXFxceHOnTucOXNGud4ODg588803Bt/2W8re3p6ffvqJ4cOHc/nyZWJiYpg+fTrz5s2jXr16lC5dGnt7eyIjI7l9+za3bt0iOTkZgM6dO2f6uFlh4sSJ+Pv7c/HiRZKTk5k5cyaLFy9Wzt+9e/c4ffq0Mj3HwcGB2bNn4+XlZXR/KpWKTz/9lI8//hiAo0eP0r59e5o1a0axYsUIDQ3l9OnTREVFUapUKd555x1+/PHHdPtob29P+/btlaDA4MGDadmyJWXLlsXe3h7Q/v87atQoG50V60yePJnz588TGBiIWq3ms88+4+eff+bll1+mUKFC3Lp1Cz8/PzQaDZ07d+bJkyecPHkSQAleCiGEEKLgsFkwIyEhgW+//Zb169crHz5BO8zb2JBkjUbDgAEDiI6Opnr16jL/VWSpIUOGEBERwa+//sqdO3eUChH6SpUqxU8//cRLL72U7r7atm3LmjVrmDRpEpcuXQK0Q/Xv3btncpvy5ctTpkwZq16DpcwNaFSoUIGNGzcyffp0du3ahUaj4cmTJ+lOWShSpIhBwr7MyOrz+MYbbxgEM9q1a4enp6dVfbZUs2bNWL16NRMnTlTyIAQHB5sMNhgrR9msWTOmTZvG119/TXJyMklJSZw8eVK5iQPtjdwHH3zA+++/z7p16yzqY/v27XF0dGT27Nk8fPiQHTt2pGlTpEgRvvnmG1q3bm3Rvo0pVqwYq1evZs6cOWzYsIHk5GRiYmI4fPiwyW2cnZ2pXbu21ce2pcKFC7N8+XImT56sTIkwdf5KlizJ7NmzMzx/r7/+Ojdv3mTBggUAREdH899//xm0eeGFF1iwYIFBEDY9n376KSdOnCA0NJT4+Pg0+ytfvnyuCWZ4enqyYsUKRo8ezZUrVwDt6Ja7d+8atGvfvj3ffPMNI0aMUJZldVJfIYQQQuQ+NglmxMTEMHjwYK5cuWJ2Jn0XFxf69+/Pn3/+yfXr1wkICEhTPUIIWxo3bhytW7dm7dq1nDlzhkePHuHg4EDFihXp1KkT77zzjtG57MbUrFkTHx8fDh8+jK+vL2fPnuXRo0fK0OdixYrxwgsv8PLLL/Pqq69Sv379HKksYG5Aw8PDg/nz53Pt2jV27NjBiRMnCAoKIiIiAjs7O4oUKaKMmmjevDktWrSweDqDMVl5Hlu3bo2jo6MyhaF///5W9zczatasyebNm/H19cXX1xc/Pz8eP35MfHw8bm5ueHl5UbduXdq2bUvLli2N7uPtt9+mQYMGLFu2jBMnTvDo0SOcnZ0pXbo0TZs2pW/fvpmqMKK//1deeYU1a9Zw9OhRpeSll5cXbdu2ZeDAgZQqVSrT+3+es7MzX331FSNHjmTr1q0cP36cO3fuEBERQWpqKu7u7lSoUIGaNWvStGlTWrVqlStvVl1dXfnpp58YMmQIW7Zs4eTJkzx69Ai1Wk2xYsWoUaMGbdq0oW/fvkYDVcaMGTOGFi1asGrVKk6fPk1YWBhubm5UqlSJLl260K9fP1xdXc0OZpQvX54tW7awatUqjhw5wp07d4iNjTX40iE3KVeuHBs2bMDHx4ft27dz/fp1oqOjKVmyJN7e3vTp04eOHTuiUqkMpuGY+7dbCCGEEPmHSmODeoyjRo1i//79gHZO9eDBg2natCmrVq1i69atqFQq5VsWfQEBAfTq1QuVSsUnn3zCe++9Z21XhBAC0CZ9HTRoEKC9ofP19ZWh6ELkE/Hx8bzyyiskJyfj4uLCmTNn5PdbCCGEKGCsHplx/Phx9u/fj0qlolq1aixdupQSJUoAGX9TUrNmTTw9PXny5Annzp2ztitCCKHQL/3Zp08fudERIh/577//lNEltWrVkt9vIYQQogCy+n9/Xa4LlUrFvHnzlECGuWrWrIlGo0mToV8IITIrNDRUKcXr4OCQY1NMhBC2FxkZyfz585Xn+lVQhBBCCFFwWB3MOHPmDCqVipdeeilTOS90VUx0pf+EEMIaKSkpzJ49Wym1+9prr1G6dOkc7pUQwhwff/wx//77r/L7+7wzZ87w9ttvK0l0S5cuTffu3bOzi0IIIYTIJayeZqIrCVitWrVMbe/s7AyAWq22titCiAJq27ZtXLhwQSlVqqtW4+joyNixY3O2c0IIs124cIGdO3fi4uJCrVq18PLywsnJiaioKC5fvmxQ2aRQoULMmTMnVyaHFUIIIUTWszqYkZqaCqDUrLdUTEwMoM0KL4QQmXHkyBE2bdqUZvkXX3xB5cqVzdrH8uXL05SAtNTLL79Mz549rdqHSF9ERAQ///yz1fsZPHiw2e8Nkf10gcnTp08bXV+yZEm+++47mjdvns09E0IIIURuYXUww9PTkwcPHiil/Cx17do14Nl0E5F7JSYmEhERoTx3cnLKdBBLCFtKSkpSHru6ulKzZk0GDhxIq1atlIBpRnbt2sWZM2es6kdkZCTt27e3ah8ifY8ePeLvv/+2ej+tWrWyOMeTyHq//vor+/bt49y5c0p56MjISBwcHPDw8MDb25vmzZvTtWtXnJ2dzf79zg1SUlIMps94eHjg6OiYgz0SQggh8jargxk1atTg/v37+Pn5kZCQgJOTk9nb3rx5kxs3bqBSqXj55Zet7YrIYhEREQQGBuZ0N4RIY8CAAQwYMCDN8qtXr5q9j7i4OKv7ERUVZdExheV0UxutFRgYmGHFLZEzGjVqRKNGjdJtY+0oqtyiVKlSOd0FIYQQIs+yOpjRunVr9u/fT0xMDKtWreLdd981e9tvv/0WjUaDSqWiTZs21nZFCCEyberUqTndBWGGkiVL8r///S+nuyGEEEIIIXKY1dVMevXqpQzVnT9/Pnv27Mlwm8TERCZPnszBgwdRqVRUqlSJDh06WNsVIYQQQgghhBBCFABWj8woXLgwkydP5tNPPyUpKYkxY8bQsWNHunbtypMnT5R2AQEBhIaGcvbsWTZu3KgMFba3t2fmzJmoVCpruyKy2PNTiCpUqICLi0sO9SbvUqvVyogkXTUfkb/INc7/5Brnf7a+xnFxcQZTNS2ZliuEEEKItKwOZgC8/vrrhISE8N1335Gamsru3bvZvXs3gBKk6N27t8E2Go0Ge3t7pk+fTuPGjW3RDZHFnk/26eLiIiXxMsHOzk75gCzBoPxJrnH+J9c4/8vqaywJtIUQQgjrWD3NRGfo0KH8+eefVKpUCY1Go/zT0V+m0WioVKkSf/zxB/3797dVF4QQQgghhBBCCFEA2GRkhk6zZs34999/2bt3LwcOHMDPz49Hjx4RExND4cKFKV68OC+//DJt2rShc+fO2NnZLJYihBBCCCGEEEKIAsKmwQzQTitp37497du3t/WuhRBCCCGEEEIIIWw3zUQIIYQQQgghhBAiO9h8ZIYoONRqtUwVygT9DPkif5JrnP/JNc7/bH2N1Wq1TfYjhBBCCC0JZohMez7JqzCP7pzJ+cu/5Brnf3KN8z9bX2N5nwghhBC2lSXBjLi4OIKDg4mJiSE5Odns7Ro1apQV3RFZRKVSybeSmaBSqZRv++T85U9yjfM/ucb5n62vsbxPhBBCCNuyWTAjJiaGFStW8M8//3Dr1i2Lv4FQqVRcvnzZVt0R2cDZ2RkXF5ec7kaepPuALOcv/5JrnP/JNc7/bHmNU1NTbdAjIYQQQujYJJhx4cIFRo8eTVhYGCBDKYUQQgghhBBCCJF1rA5mhISEMHz4cGJiYpRlhQoVomLFihQtWhR7e3trDyGEEEIIIYQQQgihsDqY8fvvvxMTE4NKpaJ48eJMnDiRTp064eTkZIv+CSGEEEKIbJKamkpMTAxRUVEkJiaSkpKS010SQgiRz9nb2+Po6EiRIkVwc3Mzu2Km1cGMQ4cOaXfk4MDy5cupWrWqtbsUQgghhMhRdnv34jh+PInffw/duuV0d7JFdHQ0wcHBMl1YCCFEtkpOTiYhIYHo6GhUKhXly5fH3d09w+1sMs1EpVLRtGlTCWQIIYQQIu/TaCg0fTp2V69SaPp06NoV8nk1EmOBDJVKJdOFhRBCZLmUlBSDkujBwcFmBTSsDmYUKVKEsLAwypYta+2uhBBCCCFy3n//YX/2LID253//QefOOdyprJOammoQyHBzc8PT0xMXFxcpKSuEECLLaTQa4uLiCA8PJyYmRglo1KhRI90pJ+ZNRklHxYoVAYiIiLB2V0IIIYQQOUujgalT0TwdkaCxt4epU7XL8yndB0fQBjK8vLxwdXWVQIYQQohsoVKpcHV1xcvLCzc3N0Ab4NAvMmKM1cGMrl27otFoOH36NMnJydbuTgghhBAiZ6SkwF9/walTqJ4mvlSlpMCpU9rRGflUVFSU8tjT01OCGEIIIXKESqXC09NTea7//5MxVgczevfuTalSpXjy5AmLFy+2dndCCCGEEFkrJQWuX4ctW+Cbb+Cdd6BePXBxgREj0rbP56MzEhMTAe2HSBcXlxzujRBCiIJMf4qj7v8nU6zOmeHi4sLChQsZNmwYCxcuRKPR8P777+PgYPWuhRBCCCEyLzUVbt+GS5cM/wUEgFpt/n70R2fkw9wZuvKr9vb2MipDCCFEjtIln05OTs6wPLhNIg5169Zl3bp1TJgwgYULF7J69WratWtHtWrVcHd3N/s/xl69etmiO0IIIYQoSFJT4e7dtEGLK1cgPt68fdjZgaMjJCQYH4GhG53RqVO+r2wihBBC5AU2Gz7h6OhIjRo1uHTpEo8fP2b9+vUWba9SqSSYIYQQQgjTUlPh3j3jQYu4OPP2YW8P1apB7dqG/+7cge7dTW+Xz0dnCCGEEHmNTYIZhw8fZuzYsajVamUUhiafzisVQgghRBbTaCAwMG3Q4vJliI01bx92ds+CFrVqPQtaeHuDk1Pa4w0frg10pDekVUZnCCGEELmG1cGMmzdvMnr0aIPkHOXKlaN69eoUKVJEcmcIIYQQwjiNBoKCjActMijHplCpoGrVtCMtvL3B2dm8ffz3n3bURUZkdIYQQgiRa1gdaVi8eDGJiYmoVCpeeOEFvv76axo0aGCLvgkhhBAiP9Bo4P5940GLDMquKVQqeOGFtEGLmjWhcGHr+jZ1qnYkR2pqxu3t7GR0hhBCCJELWB3MOH78OADOzs4sXbqU0qVLW90pIYQQQuRBGg08eGA8aBEZaf5+TAUtsqJsaGKiNg+HOYEM0LYLDNRu9/x0FSGEEEJkG6uDGWFhYahUKpo1ayaBDCGEEKIg0GggJCRt0OLSJYiIMH8/lSqlDVq8+CK4umZZ19NwctJOHQkNNVgcr1cFpfDzIz9KlZJAhhBCCJHDrA5meHh4EBYWRokSJWzRHyGEEELkFhoNPHpkfKRFeLj5+6lY0XjQws0t6/puiQoVtP/0aOLi0Gg02sTmWTEiRIgMeHt7K4+vXr1q9T4scerUKYoUKWKwbNCgQZw8edJo+0KFCuHu7k6lSpWoX78+vXv3pkaNGhYfV6PRcODAAfbv38+ZM2cICwsjKioKd3d3SpQoQYMGDWjTpg2tW7fGzs4uw/0tWLCAhQsXmlzv4OCAm5sblSpVomHDhpnq961bt9i1axdHjx4lKCiI8PBw7O3tKV68OFWrVqVly5a8/vrreHp6Gt3+xIkTDB482KJjmjJmzBjGjh1rk30JkRdYHcx44YUXCAsL4/Hjx7bojxBCCCFyQmio8ZEWYWHm78PLK23QolYtcHfPun4LIXJcUlIS4eHhhIeHc+7cOZYuXcrAgQOZNGmSWUEH0N7Uz5kzhytXrqRZp9v3tWvXWLNmDd7e3kyaNImmTZta1e/k5GQiIiKIiIjg/PnzLFu2jKFDh/L5559n2O/w8HDmzZvHli1bSDFSBSk2NpZ79+6xb98+fvjhB0aMGMH777+Pvb29VX0WQjxjdTCja9eunDp1ilOnThEXF4eLfHshhBBC5F6PHxuOsNA9fm6aRbrKlzc+0qJo0azrtxDCKosWLTK7bZqpVc8ZN26cwQiGxMREHjx4gK+vL2fPnkWj0bBy5UoKFSrExIkTMzzemjVrmDlzphIUKFasGB06dKBWrVp4eHgQGRnJlStX8PX1JSwsjKtXrzJ8+HCmTZvGW2+9ZdZr6tKlC127djVYlpiYyMOHDzl48CDHjh0jNTWVv/76C0dHRz755BOT+7p16xbvv/8+9+7dA8De3p5mzZrRrFkzypQpQ1JSEkFBQezbt49Lly4RGxvL/PnzOXfuHD/++CNueqPSqlevnu61OX78OCtXrgSgSZMm6Y7ieOGFF8w6F0LkF1YHM/r06cPq1au5du0a3377LV999ZUt+iWEEEIIa4SHGx9p8eiR+fsoW9b4SAsPjyzrthAia3To0MFm+2rYsCFNmjRJs/zdd9/lzz//5LvvvgNgxYoVDBo0iHLlypnc144dO5g+fbryfPDgwXz88ce4Gsmd88UXXzB//nyWLVtGSkoK06dPp0iRInTp0iXDPlepUsXkORg+fDjr169nypQpAPz555+8++67aabagHZExrBhw3j48CEAtWrVYs6cOdSsWTNN27Fjx+Lr68vUqVMJDw/n4MGDfPrpp/z+++/aKWyAp6dnutcmSq/iU7ly5Wx6HYXI66wOZjg6OrJw4UJGjhzJunXrSEhIYMKECSbnhQkhhBDChp48MT7S4ukHbbOUKZM2YFG7NhQrlnX9FkLkS++++y7bt2/n8uXLJCcnc+DAAd5++22jbYOCgpg6dary/JNPPmHUqFEm9+3i4sKXX35JsWLF+PHHHwGYMmUKdevWxcvLy6p+9+/fn1WrVhEQEEBSUhLnzp2jdevWadpNmjRJCWS89NJLLF++3GCkxfM6dOhApUqVeOedd4iMjOTAgQMsW7aMYcOGWdVfIYQNghm6pDpt2rThf//7H1u2bOGff/7hlVdeoXr16rhbME92zJgx1nZHCCGEyJ8iI42PtHjwwPx9lCplfKRF8eJZ128hbMnXFz76CH7+GeQb6lyrUaNGXL58GYA7d+6YbLd48WJiY2MBaN68ebqBDH2jRo3i+PHjHDt2jNjYWJYsWWKT0eHVqlUjICAAQOmXPj8/P/bt2weAs7MzP/zwQ7qBDJ3q1aszadIkZcrNb7/9xltvvZXhdB4hRPpsEszQDZPSSUxM5NixYxw7dsyifUkwQwghRF5gt3cvjuPHk/j999Ctm213HhVlfKRFcLD5+yhZMu0oi9q1QSqPibxMo4FJk+DKFe3P9u3huc+gIndw0itdrFarjbaJiopi8+bNyvNx48ZZdIyPPvpIudfYtGkT48ePt+hLVGOePHmiPC5btmya9StWrFAe9+zZk8qVK5u97169evHrr79y584dIiIi2LJli9n5PoQQxlkdzABtGSVzlqXn+YCIyP3UarXZGarFM2q1+lm5P5EvyTXO5zQaHKdNw+7qVRymTSOubdvM3VBFR2MXEIDq8mXsrlzB7soVVFeuYGdB0EJTvDipL75I6osvonn6M/XFF7XBDGPi4izvZwFl699jUzd0wgL//QenTmkfnzqlfd65c872SRh1/fp15bGpfBmnTp0iISEBgMqVK1OvXj2LjtGgQQMqV67MnTt3SEhI4PTp07Rt2zbTfb5165ZSetbT0zNNDgyNRsORI0eU571797b4GL1791amxxw9elSCGUJYyepghoymKLg0Go3FQSvxLNAn5y//kmucv9n7+uJw7hwADufOkezrS0p6w91jYrALCNAGK57+tLtyBbvAQLOPqfH0JLVmTe0//aBFqVLGAynyvrOarX+P5W+BlTQamDoV7O0hJUX7c+pU6NRJRmfkMhcvXuTgwYPK84YNGxptd/bsWeVxgwYNMnWs+vXrK9NYzpw5Y3EwIzExkZCQEA4dOsSiRYtISkpCpVIxfvz4NFNAbt26RUREBKDNGVi7du1M9VfnzJkzFm8vhDAkwQyRaSqVSr55zgSVSqV82yfnL3+Sa5yPaTQUmjkTjb09qpQUNPb2FJo5k9QOHSAuDrurV9OOtHhaus+s3Xt4GB9pUbp0mhs2eWdlLVv/HsvfAivpj8oAbUBDRmdYxNvb26x2vXv3Zu7cuRbtW1eadc+ePfzyyy9KidVXXnmFV155xeg2D/WSFGe2pGiVKlWUxyEhIem2XbhwoZLrzxh7e3uaNGnCu+++azTxp35/vby8cHR0tKq/jx8/Jjk5GQcHmwyUF6JAkt8ekWnOzs64uLjkdDfyJN0HZDl/+Zdc43xq1y7Q+zZRlZKC/dmzuFStqi15au6370WLps1nUbs2qrJlsVepsM+i7gvL2PL3ODU11QY9yiXWr4dp0yA6OnuOp9FAaKjxdd27a6dVZVewyN0dZs2Cfv2y53i51ODBgzNs4+3tzYIFC0yuj4yMVB4bK4FqDv0cGbpRE5llZ2eHo6OjySCFLfr7/HaRkZEUlwTMQmSaBDOEEEKI9CQkaL8BPnAAvvvOeBtT3wi6u6etHlK7NpQrJ0PjRd41bx48rfiQ45KS4P797D3mvHl5MpixaNEis9oZS3xpCQcHByZNmkT//v0zNXohq3Tp0oWuXbsaLEtJSSEiIgJ/f3/++ecfDh06xKFDh/jggw/4+OOPc6ajQgizSTBDCCGE0BcdDceOwcGDcOgQnDihDWhkpEYNaNHCMGjh5SVBC5H/TJigzVeRHSMzdKMykpJMtylUKPtGZ7i7w+efZ/1xskAHG5ayHTduHDVq1AC0AYFHjx5x6tQpdu/eTXJyMosXL6ZRo0ZKG2OKFi2qPI6KispUP6L13oMeHh7ptq1SpYrJc/Dmm28yduxYhg0bxo0bN/j111+pVq0a3fSqVdmiv89vp79PIYTlJJghhBCiYAsNhcOHtYGLgwfBz087H98S9vbaqSN//inBC5H/9euXfSMTdu2C115Lv01SEvz1l+TOyEYNGzakSZMmBssGDRrEmTNnePfdd3n48CHDhw9n8+bNlDBRErpMmTLK49u3b2eqH7du3VIely5dOlP70ClVqhTTpk1TptAsWLDAIJih39+goCASExMtHnmi398SJUpIvgwhrGTWb5B+DWjQ1kk2tc4a+vsVQgghskRg4LNRFwcPwpUr6bevUgWqVoXdu023kWSEQtje8xVMTJHKJrlGw4YNmTRpElOnTiU0NJSpU6fy66+/Gm2rX8FEv7KJJc49rSylO7a1GjVqROHChYmPj+fOnTvcv39fKS1bpUoVPDw8iIiIIDExkUuXLhlUJzGHn5+fTfsrREFnVjDjiy++ULJwq1Qqg6CD/jprPL9fIYQQwmoaDVy7Zhi8uHs3/W1eeglatYKWLbX/ypWDJk3khkqI7PZ8BRNTJJiYq/Tv35/Vq1dz+fJl9u7dy7Fjx2jWrFmado0aNcLJyYmEhATu3LnD+fPnefnll80+zrlz55SyrE5OTiarpljCzs4Od3d34uPjAW2FFF0wQ6VS0aJFC3bs2AFov9C1NJixadMm5XGLFi2s7q8QBZ2duQ11ddaN1UnXX2fNPyGEEMIqKSnaaiPz50PfvlCmDNSsCe+9BytXpg1kODhoAxXjx8OWLRAWBhcvwqJF8NZbUL78sxuqjKae6N9QCSGsoxuVYWfmR1U7O217+TyZ41QqFWPHjlWe//DDD0bbFSlSxOCLzJ9//tmi4+hXSunTp49BZZPMSklJMchrUbhwYYP1gwYNUh5v3ryZuxkFx/Vs3bpVmU7j4eFBjx49rOytEMKskRm9e/fO1DohhBAiS+kqjehGXRw9CuklZnN2hmbNtCMuWrWCpk3B1dV0e/0bKnNKa+puqGR0hhDWSUyEe/fM+70DbbvAQO12Tk5Z2zeRobZt2+Lt7c3Vq1e5ePEie/fupV27dmnajRw5km3bthEXF8fhw4dZvHgx7733Xob7X7x4MUeOHAHA1dWVkSNH2qTfJ0+eRK1WA+Do6EjFihUN1tevX582bdqwf/9+1Go148ePZ+nSpbi5uaW735s3bzJ79mzl+ahRo9IESoQQljMrmDFnzpxMrRNCCCFsSldpRBe8yKjSSNGi8Oqrz4IXDRuCJQnb5IZKiJzh5KQNVIaGmr9NqVLye5dLqFQqRo0axSeffAJoR1G0bds2zdT0ChUqMGvWLD777DNAO4ojPDycjz76CBcXlzT7jY+P5+eff+avv/5Sln399deUL1/e6j6HhIQwc+ZM5Xm7du2M9mHOnDn06tWLkJAQLly4wODBg5k7d67Jyi379u1j8uTJREREANC6dWuGDh1qdX+FEBZUM9El+qxSpQp169bNqv4IIYQQzzx+bFhp5Ny59Kd7lCnzLHDRsqU2/4W9feaPb+KGSjefGtIOQ5YbKiFspEIF7T/Bjz/+aFa7UqVK8c477xhd5+vra/bx6tatS6lSpcxub8xrr73GggULuHXrFpcvX2b37t106tQpTbtu3boRHR3NrFmzSElJYenSpWzZsoWOHTtSq1YtihYtSmRkJFeuXGH37t2EhYUBYG9vz9SpU+nSpYtZ/bl161aac5CamkpERAQXL17kn3/+ISYmBgBPT08mTJhgdD+enp4sW7aM9957j8DAQC5dukTv3r1p1qwZzZs3p1SpUiQnJxMUFMS+ffvw9/dXtm3ZsiX/93//Z5N8g0IIC4IZukSf77zzjgQzhBBCZI3AwGeBi0OH4PLl9NtXqWIYvKhWzfbTO4zcUGni4tBoNNoPpEa+uRNCCFv67bffzGpXs2ZNk8GMDz/80OzjLVq0iA4dOpjd3hg7Ozvef/99Jk6cCGhHZ3Ts2NHojfzbb7/NCy+8wJw5cwgICCA8PJy1a9ea3Le3tzeTJk2iadOmZvfnn3/+4Z9//smwXc2aNfnhhx/SHe1RpUoV1q1bx3fffceWLVtITk7m0KFDHDp0yGh7V1dX3n33Xd5//30pxyqEDclvkxBCiJyhqzSiH7x4mpnepJdeMgxe2GBosRBCiKzRrVs3FixYQFBQENeuXWPnzp0mR1I0bdqUzZs3c+DAAfbt28fZs2cJDQ0lOjoad3d3SpQoQYMGDWjTpg1t2rTBztzksOlQqVS4urpSqlQpateuTefOnWnbtq1ZAQdPT0/mzp3Le++9x7///suRI0cICgriyZMn2Nvb4+npSfXq1WnZsiWvv/46np6eVvdXCGFIpTGzjEjNmjWVkRlTpkzJ6n6JXCgmJoarV68qz729vTNMeCTSitP7RtfYXEyR98k1NiElBS5ceBa4OHQIHj0y3d7eXpvjQhe4aNECihfPvv6mQ65x/mfra5yb/w+9fv06ycnJODg4UL169ZzujhBCiALO3P+XZGSGEEKIrJGQAKdPPwteHDmScaWRpk2fBS+aNoVccrMnhBBCCCFyFwlmCCGEsI2YGG2lEV3w4sQJeFrizij9SiMtW8Irr1hWaUQIIYQQQhRYEswQQgiROfqVRg4dgrNn0680Urr0s1EXrVpZX2lECCGEEEIUWBLMEEIIYR5dpRFdws6MKo288IJh8CIrKo0IIYQQQogCSYIZQggh0tJo4Pr1Z1NGDh40v9KI7p+XV7Z0VQghhBBCFDwWBzOOHTvGl19+afOOqFQqvvnmG5vvVwghhBl0lUb0y6SaU2lEN+oiF1UaEUIIIYQQ+Z/FwYxbt25x69atrOiLBDOEECK76CqN6IIX5lYa0QUvpNKIEEIIIYTIQRYHMzQaTVb0A5XMoxZCiKyjqzSiC16YU2mkRYtnwYuGDcHJKfv6K4QQQgghRDosDmZUqVKFl19+OSv6IoQQwlbCwrSVRnRTRsypNKILXLRsCXXqSKURIYQQQgiRa1kczGjevDlTpkzJir4IIYTIrKAgw3wXly6l3/6FFwyDF9WrS6URIYQQQgiRZ0g1EyGEyGt0lUb0gxe3b6e/Te3azwIXUmlECCGEEELkcRLMEEKI3C4lBS5efBa4OHQIQkJMt7e3hwYNngUvXn1VKo0IIYQQQoh8RYIZQghhY3Z79+I4fjyJ338P3bpZvoPERG2lEV3w4sgRiIw03V6/0kjLltCsmVQaEUIIIYQQ+ZoEM4QQwpY0GgpNn47d1asUmj4dunbNOBdFTAwcP/4seHH8ePqVRooU0Y62kEojQgghhBCigJJghhBC2NJ//2F/9iyA9ud//0HnzoZtdJVGdDkvMqo0UqrUsykjrVpJpREhhBBCCFHgSTBDCCFsRaOBqVPR2NujSknR/pw6VZt8U5fr4uBB8yuN6IIXUmlECCGEEEIIAxYFMzQaTVb1Qwgh8r7//oNTp9CFHVQpKXDqFFSokP52tWsblkmVSiNCCCGEEEKky+xgxooVKwAoXbp0lnVGCCHyLLUaRo/OuJ2u0ogueNGiBZQokfX9E0IIIYQQIh8xO5jRuHHjrOyHEELkTVevwpIl2n9RUabbvf02DBsmlUaEEEIIIYSwAcmZkQclJiaydOlStm7dSmBgIC4uLrzyyit88MEH1K5dO6e7J0T+Fx8PGzdqAxgHD2bc3t4ebtyADh0k94UQQgghhBA2YJfTHRCWSUxM5N133+X//u//ePLkCW3btqVKlSrs3r2bN998k0OHDuV0F4XIv/z9Ydw4KF8eBg0yL5AB2kolp05pc2oIIYQQQgghrCYjM/KYJUuWcPLkSerUqcOyZctwezpcffv27Xz22Wd8/vnn+Pr6KsuFEFaKi4N162DxYjh2LO16b29tm/v30y+vam8PU6dCp04yOkMIIUSGvL29Ta4rXLgwRYsWpVq1ajRt2pTevXtTwoz8S4MGDeLkyZOANh9ekyZNLO6X/j702dnZ4erqiru7O8WKFcPb25tatWrRunVrKlasaNExEhMT8fX1xdfXl0uXLvH48WPi4+NxcnKiRIkSVKxYkZo1a1K/fn2aNm0qn3uFKKAkmJGHJCcnK4lYp0+fbvCHu1u3bmzdupUDBw6wceNGhgwZklPdFCJ/OH9eG8D4+2+IjDRc5+wM/fvDe+9BTAy8/nrG+9MfndG5c9b0WQghRIEQHx9PfHw8Dx8+5PDhw/z6669MnTqV3r1751ifUlNTiY6OJjo6mvv373Pp0iV8fHyYPXs2jRo1YvTo0TRr1izD/Vy4cIEJEyZw+/btNOvi4uK4d+8e9+7d4/DhwwAUL16co0eP2vz1CCFyPwlm5CFnz54lIiICLy8v6tSpk2Z9ly5dOHDgAHv27JFghhCZERMDa9ZogxinTqVdX6cOjBwJAwdCsWKg0UCTJmBnB6mpGe/fzk5GZwghhLDYokWLDJ7HxcVx69Yttm/fTmBgILGxsXz55ZcULVqUdu3aZVu/xo0bR40aNZTn8fHxREVFERQUxPnz5/Hz8yMlJYWTJ09y6tQpBgwYwOTJk7G3tze6P39/f4YMGUJcXBwAJUuWpHPnznh7e1OkSBHUajUhISFcunSJY8eOERUVRUp6oyKFEPmaBDNMSElJ4ebNm/j7+3Pp0iX8/f0JCAhArVYD0Lt3b+bOnWvxfvfs2cOWLVvw9/cnNDQUNzc3KlWqRIcOHXjrrbfSHSZ35coVAJNJPmvVqgXA1atXLe6XEAXamTPaAMb//qcNaOhzcYG33tIGMZo0MQxCJCbCvXvmBTJA2y4wULudk5Pt+i+EEAXEgVkH2D99P22+akPrqa1zujvZpkOHDkaXjx49mvHjx7Nr1y40Gg3fffddtgYzGjZsmO5UleDgYH7//XfWrl2LRqPh77//JjU1lRkzZhhtP23aNCWQ0bt3b7766iucTPx/mZyczNGjR9m5c6fVr0MIkTdJMMOEjz/+mP9smKwvNjaW8ePHs3fvXoPl4eHhhIeHc+7cOVatWsVPP/1EvXr1jO7j/v37AJQpU8boet3yiIgIYmNjcXV1tVn/hch3IiO1wYslS+DcubTr69WD99/XllQtWtT4PpyctCM4QkMNFsfHxyuPCxcubLhNqVISyBBCiEw4MOsA+6ftB1B+FqSAhjGOjo7MmDGDvXv3kpSUxO3bt7l58yZVq1bN6a4BUL58eWbOnEmDBg2YOHEiAKtXr6ZJkya8/twUzRs3bnDp0iUAypYty6xZsyhUqJDJfTs4ONCqVStatWqVdS9ACJGrSTDDhOeHrHl4eODh4cGdO3cyta9x48YplUZKlChB//79qVatGpGRkWzfvp2zZ8/y4MED3nvvPVavXm30PyFdpDrNzdFTLi4uymMJZghhhEYDJ05oAxhr1mgTd+pzc4MBA7SjMBo2NG8qSIUK2n/6h4mLQ6PRoFKptCM7hBBCWEU/kKEjAQ0tT09PqlWrpozgvXPnTq4JZuj06tWLy5cvs3z5ckA7baZz587Y2T0rrHjr1i3lcb169dINZAghBEgww6S6detStWpVateuTe3atalQoQI+Pj58+eWXFu9r/fr1SiCjWrVqLF++3CDj9DvvvMO3337LX3/9RWRkJNOmTePvv/+22WsRosB78gRWrdIGMS5eTLu+USNtMs+33tIGNIQQQuQaxgIZOhLQ0NKfipGQkJCDPTFt1KhRrFmzhoSEBK5fv46fnx8NGjRQ1icnJyuPw8LCcqKLQog8RoIZJowaNcom+0lJSWHhwoXK8++++85o6azx48dz7Ngxrly5wunTpzl8+DCvvvqqQRvdyAv9Iez64vS+ZZZRGaLA02jgyBFtLoz16+FpvhtFkSLaRJ4jR2qnlAghhMh10gtk6BT0gEZycrJB5Y+yZcvmYG9M8/T0pEWLFsqU65MnTxoEMypVqqQ8PnfuHBcuXKBu3brZ3k8hRN5hl3ETYY1Tp04R+nQ+fePGjU0m77S3t2fQoEHK8x07dqRpU65cOQAePnxodB+65R4eHhLMEAVXWBj8+CPUrg0tW8LKlYaBjObNYelSuH8fFi2SQIYQQuRS5gQydPZP28+BWQeytkO51KpVq4h8WkLc3d2d6tWr53CPTKtfv77y+OJzIyVr1aqlTI9JSkpiyJAhfPfdd/j5+ZGUlJSt/RRC5A1Wj8w4Zax8oQVUKhVubm4UKVJEuVnPTw4ePKg8zihBkf56/e10XnzxRQAlOdLzLl++DIC3t7fF/RQiT9NoYP9+7TSSjRu11UL0FSsGgwfDiBHw0ks50kUhhBDmsySQoVOQRmjEx8dz69YtNm7cyOrVq5XlgwYNSrcyXk7T/6wfHh5usE6lUvHNN98wdOhQ4uPjiYuL488//+TPP/+kUKFCeHt7U7t2bRo0aECzZs0oXbp0dndfCJHLWB3MGDRokDbJnQ0ULlyY2rVr0717d7p162aQ0DKvunbtmvK4Tp066bYtWbIkZcuW5cGDBzx+/Jjw8HA8PT2V9Q0aNMDDw4OgoCAuXryYZn///PMPAO3bt7fhKxAiF3v0CJYtgz/+gOvX065v1UqbC6NPHzCROFcIIYRlLq2/xP5p+0mIzprcDAlRCSRGJ2bc0Ij90/ZzdN5RnIpkTdUoJ3cn2s5qS61+tbJk/6aY80VVjx49GDNmTDb0JvOKFCmiPI6IiEizvl69eqxfv55Zs2Zx4sQJZXlSUhL+/v74+/uzdu1a7OzsaNq0KWPGjKFhw4bZ0XUhRC5kk5wZGo3GFrshLi6O06dPc/r0aX799Ve++eYbmjVrZpN95xT9OYxeXl4Ztvfy8uLBgweANquzfjDDwcGBwYMH8/PPP/PVV1+xbNkyJfq+fft2Dhw4QLFixejbt6+NX4VxarXaIAu1MI9arX5W6UJYLjUVu337cFi6FPvt21E9N/RUU7w4ye+8Q/LQoWh0H/40mrSVS7KQXOP8T65x/mfra6x+Pm9PHnZ03lEeBzzO6W6YlBidmOlgSEaiiebovKPZHsxIT8mSJfn2229p0aJFTnclQ/r3DKZ+t6pXr86KFSu4fv06u3bt4syZM1y8eJHo6GilTWpqKkePHuXYsWN89NFHjB49Osv7LoTIfawOZjRq1Eh5fP78eZKSkpQ/VMWKFaNMmTK4uLgQHx/Pw4cPlSFlKpUKR0dH6tatS3JyMpGRkdy7d0/JZKwrU7pkyRKaNm1qbTdzjP4f3mLFimXY3sPDw+i2OiNHjuT48eOcPHmSTp060ahRIx4/fszp06cpVKgQ3333XbYNL9RoNDYLZBUkunMm588yqocPcVi5Eofly7EzUiI5pXVrkoYNI6V7d9Bldc+h8yvXOP+Ta5z/2foa56f3SYsJLdg3dV+uHJkB4OjumKUjM5p/3jxL9p2eRYsWKY8TExO5f/8+//33H+fPnyc0NJRff/2VunXr4u7unu19s0RUVJTyWP8zrzHVq1dX8n9oNBoCAwPx8/PjwIED7Nq1S7nnmD9/PhUqVKB79+5Z2XUhRC5kdTBj5cqVxMbGMmnSJBITE3Fzc2PYsGH06NGDChUqpGkfHBzMli1bWLp0KTExMRQvXpxvvvkGFxcX1Go1u3btYv78+dy/f5+kpCQmTpzI7t27cXR0tLarOUK/woh+2SxT9NvExsamWe/o6Miff/7JX3/9xdatW9m7dy8uLi60b9+eDz/80GSC0aygUqnkW8lMUKlUyrd9cv4ykJKCna+vdhTGP/+gSkkxWK0pWZLkQYO0ozCeJg3LDWdUrnH+J9c4/7P1Nc5P75Na/Wpl+ciEzOTMAGgzs02+zJnRoUOHNMtGjBjBsmXLmDNnDqdOnWLs2LH89ddfuXrUbHBwsPJYf/RxRlQqFRUrVqRixYr06NGDjz/+mBEjRnDn6ZcbCxYskGCGEAWQTaaZTJw4kT179lCpUiWWLl2abiLP8uXLM3r0aHr37s3QoUPZtWsXKSkpLFiwAGdnZ3r27EmbNm0YMGAAN2/e5NGjR2zevJk33njDFl3NFxwdHRk1apTNysdmlrOzc77Ia5ITdB+Q5fyZEBQEf/0Ff/4J9+4ZrlOpoFMnGDkSVffuFHJ0pFDO9DJdco3zP7nG+Z8tr3FqaqoNelRw6AISlgQ08msgIz1Dhw7l4sWLbN++nWPHjrFixQqGDh2a090yyc/PT3lsTdnVChUqMHfuXN566y0A7t69S1BQkFlTuoUQ+YfVoVtfX198fX1RqVTMnz/f7IokZcuWZf78+Qb70ClatCgzZ85Unh86dMjabuYY/Q9ACQkZD8fUbyPlVUWBkpwMW7dC9+5QqRJMn24YyChbFqZMgVu34N9/oW9fyKMjtoQQQmSs9dTWtJnZxqy2BTGQoTNx4kScnZ0B7XSUJ0+e5HCPjAsLC+PIkSPK88aNG1u1v3r16hl8zg4NDbVqf0KIvMfqYIaPjw+gja7WrFnTom1r1qxJvXr10Gg0yn50GjZsSKVKldBoNErJ0bxIf+6iOf+56Gd2zu3zHoWwibt3Ydo0bQCjZ0/Yvh1032Da2UHXrrB5szawMWsWVK6ck70VQgiRjcwJaBTkQAZAqVKlePvttwFtTorFixfncI+M++2330h8Wjrd29ubl19+2ar9qVQqHByeDTKXUXJCFDxWBzMCAgJQqVRUfTpf3VJVqlRR9vO8WrW08zFza4TZHC+88ILyOCgoKMP2+m1050aIfCcpCXx84PXX4YUXtEGK+/efrffyghkz4M4dbXCjZ09wsMmsOCGEEHlMegGNgh7I0Bk+fLiSX2716tU8fpy7qs1s3ryZFStWKM/HjBmTJo9MVFSUEuwwx8mTJ5WEos7OzlSsWNE2nRVC5BlWBzN0fywt+eOjL+lpWUVjf3R1tah1FU7yoho1aiiPL168mG7bx48fK2VZixcvblFiJCHyhJs34csvoUIF7TSRf/99VnHE3l4btNixQxvEmD5d204IIUSBZyygIYGMZ0qVKkXfvn0BiI+PzzWjM+7fv8+0adOYOHGismzgwIF06tQpTVs/Pz/at2/PH3/8waNHj9Ldb0BAgME+O3XqROHChW3XcSFEnmD1V53u7u6Eh4dz4cKFTG1//vx5ZT/P0+WPyKh0U27WsmVL/vzzTwAOHjzIyJEjTbY9cOCA8rh169z/n7Narc7VGbNzK7VarSSVKxASErDfvh2Hv/7Cfv/+NKtTK1UieehQUgYNQlO2rLJNXlbgrnEBJNc4/7P1NVar1TbZT0GmJAWdvp82X0kg43kjR45kw4YNJCUlsWbNGt59911Kly5tsv2GDRs4evSoWfsePXq00ap8Z86cITo6WnmuVquJjo4mMDCQ8+fPc+7cOVKeViJTqVQMHDiQSZMmmTzOo0ePmDdvHj/88AMvv/wy9erVo3LlyhQtWpSUlBQePHjAqVOnOHz4sLLfMmXK8Pnnn5v1OoQQ+YvVwYwaNWpw7Ngx7t27x44dO+jatavZ2+7YsYO7d++iUqmUOtL6dFMuihUrZm03c0zjxo0pWbIkoaGhnDx5kkuXLhktn5qSksLKlSuV5126dMnObmaKRqNBo/tWXZhNd87y+/lTXb+Ow7JlFFq1ClVYmME6jYMDKV27kjx8OClt22pzY8CzURp5XEG5xgWZXOP8z9bXWN4nttF6amsJYphQvnx5unfvjo+PDwkJCfz+++9MmzbNZPutW7eave93333XaDBDl8w/PSqVikaNGvHhhx/StGlTk+2KFy9OqVKlePToEampqZw7d45z586lu++mTZsyZ84cSpUqlfGLEELkO1YHM7p06cKxY8cAmDx5MnZ2drz++usZbrdr1y6mTJmiPH8+CJKYmMjly5eVutJ5lb29PaNHj+arr74CtBmnly9fTvHixQ3aff/991y5cgWABg0a0LJly2zvq6VUKpV8K5kJKpVK+bYv350/tRr7zZtxWLYMeyNViFKrViV5yBCSBw6Ep98W5bMzAOTzaywAucYFga2vsbxPRHZ4//332bJlCykpKaxfv56RI0dSVjfqMYvZ2dnh4uKCm5sbnp6eeHt7U7t2bVq3bm3WZ/natWtz8OBBLl68yIkTJzh//jy3b98mJCSEuLg4HBwccHd3p1KlSrz00kt07tyZhg0bZsMrE0LkViqNlV8VpKam0q9fP65cuaL8p1+/fn169OjByy+/TJkyZShcuDDx8fGEhIRw/vx5tm3bxpkzZ5T2L774Ihs2bDCYsrBr1y7GjRuHSqVi4sSJ2V4zOzAwkA0bNhgsu3r1Kvv27QO0WZjbtm1rsL5p06Y0a9Yszb6Sk5N57733lHJUJUuWpH///lSrVo2IiAh27NjBmTNnAG2ekP/9739GR6rktJiYGK5evao89/b2xs3NLQd7lDfFxcUp7/18k3n70iVYsgRWroTwcMN1jo7Qpw+MHAlt2jwbhZGP5ctrLAzINc7/bH2Nc/P/odevXyc5ORkHB4dc+flDCCFEwWLu/0tWj8yws7Pj119/ZfDgwdy9exfArGFhOl5eXvzyyy9pci/8+++/lCtXDoCOHTta202L3b9/n99++83k+qtXrxp8KAFwcHAwGsxwcHDg559/Zvz48ezbt4/Q0FB++eWXNO3KlCnDjz/+KB8kRN4QFwfr18PixWBszq23N7z3HgweDCVKZH//hBBCCCGEEPmWTWodli5dmrVr1zJ79my2bdtm9nbdunVj8uTJRnNi/Pjjj7boWq7h5ubGb7/9hq+vL1u2bOHixYuEhYXh6upKxYoV6dixI2+99ZbRRKhC5Crnz2tHYaxaBZGRhuucnKB/f20Q49VXQYZVCyGEEEIIIbKATYIZoK04Mm/ePEaNGsXGjRs5efIkV69eVUqvgnaEgre3N40bN6Zfv35UrVrVVoe3uSZNmqQZeWELHTp0oEOHDjbfrxBZKiYG1q7VjsI4eTLt+pde0k4jGTgQpKSwEEIIIYQQIovZLJihU7VqVSZMmKA8j46OJi4uDhcXFxl1IERec+aMdhTG//4HeqXXAChcGN56SxvEaNpURmEIIYQQQgghso3NgxnPc3d3lyBGPqVWq9PkOhEZU6vVSlK5XCkqCof163H46y/s/PzSrE6tW5fkYcNIfvNNKFpUuzA+Pnv7mMvl+mssrCbXOP+z9TVWq9U22Y8QQgghtLI8mCHyL41Gg5XFcAok3TnLVedPo8Hu9Gkcli7FYcMGVHFxhqtdXUnu35/k4cNJrV//2SiM3NL/XCZXXmNhU3KN8z9bX2N5nwghhBC2JcEMkWkqlUq+lcwElUqlfNuX4+cvIgKHNWtwWLoUO3//NKtTGjQgefhwUvr1g6cjrOSKZyxXXWORJeQa53+2vsbyPhFCCCFsK8uCGXFxccTExJCcnGz2NrpSrCJvcHZ2xsXFJae7kSfpPiDnyPnTaLSlVBcv1pZWfX6KSJEi8M47MHIk9vXrY5/9PcwXcvQai2wh1zj/s+U1Tk1NtUGPhBBCCKFjs2BGamoq27ZtY8eOHVy8eJGIiAiLtlepVFy+fNlW3RFCPC8sDFau1AYxrlxJu75pU21J1TfeAFfX7O+fEEIIIYQQQpjJJsGMoKAgPvzwQ65duwbIvFAhcg2NBg4c0FYk2bgREhIM13t4wKBB2ookderkSBeFEEIIIYQQwlJWBzPi4+MZOnQoQUFBBsudnZ0pUqQIDg6SlkOIbPfoESxfDn/8AU+DjAZattQGMPr105ZYFUIIIYQQQog8xOpIw4oVKwgKCkKlUmFvb8/gwYPp27cvVatWtUX/hBDmSk2FvXu100g2b4akJMP1xYvDkCEwYgS8+GKOdFEIIYQQQgghbMHqYIavr6/y+IcffqBz587W7lIIYYmHD2HpUu0ojFu30q5v21abC6N3b3Byyv7+CSGEEEIIIYSNWR3MuHv3LiqVilq1akkgo4BRq9XY2dnldDfyHLVarWTIz7SUFOx8fXFYtgz7f/5B9VzVIE2JEiQPGkTy0KFoqlVTtiEuzoqeC3PZ5BqLXE2ucf5n62usVqttsh8hhBBCaFkdzEhMTATgRRm2XuBoNBpJ9poJunOWmfOnun8fhxUrcFi+HLvAwDTrU9q1I2nYMFK6dgVHR90Bre6zsIw111jkDXKN8z9bX2N5nwghhBC2ZXUwo3Tp0ty7d4/k574ZFvmfSqWSbyUzQaVSKd/2mXX+kpOx/+8/7Jcuxf7ff1Glphqs1pQuTfLgwSQPGYLmhRe0x8iKjguzWXyNRZ4j1zj/s/U1lveJEEIIYVtWBzMaNWrE3bt3lbKsouBwdnbGxcUlp7uR59jt3Yvj+PEkfv89zt26mW549y78+Sf89RcEBxuuU6ng9ddh5EhUXbtSqFAhCmVtt4WFdDdB8juSf8k1zv9seY1TnwtECyGEEMI6Vic8eOutt7Czs+PKlSv4+/vbok9C5F8aDYWmT8fu6lUKTZ+edgpIUhJs2gRdusALL8CsWYaBDC8vmD4d7tyBHTugVy8oJGEMIYQQQgghRMFidTDjpZdeYtSoUWg0Gj777DMeP35si34JkT/99x/2Z88CaH/+9592+a1bMGkSVKwIffrAzp3PAh329tCjB2zfrg1izJihbSeEECLLHJ17lO/dv+fo3KM53RUhhBBCGGH1NBOAjz76CDs7OxYtWkT37t0ZNWoUr732GqVLl7bF7oXIHzQamDoVjb09qpQU7c8PP4TKlWHPnrTtK1WCESNg2DAoXz7buyuEEAXVgVkHODLrCABHZh2hUKFCtJ7aOod7JYQQQgh9Vgcz2rdv/2xnDg48efKEuXPnMnfuXNzd3XFzczMr6ZVKpcLX19fa7giRe/33H5w6pSTnVKWkwM2b2n86Dg7aURjvvQcdOmhHZQghhMg2B2YdYP+0/QbLdM8loCGEEELkHlYHM4KDgw2CFbrHGo2GqKgooqOjM9yHLeu4C5ErPR2VgZ0dGEsCV6UKjBwJQ4dCmTLZ3j0hhBDGAxk6EtAQ2c3b29ui9o0bN2blypVZ1Jus5+/vT9++fQHw9PTk4MGDFLIwL9jOnTv5+OOPAahTpw4bNmxQ1g0aNIiTJ08CsGLFCpo0aWKbjgN//PEH8+bNU57/9NNPvP766zbbv47+a9BnZ2eHq6sr7u7uFCtWDG9vb2rVqkXr1q2paObUZB8fH7788kuDZUuWLKFVq1Zmbf/ZZ5+xfft2g2VXr141a1shMsvqnBnwrAa7/r/01plqK0S+tW0bnDplPJABsHAhfPGFBDKEECKHpBfI0Nk/bT8HZh3Ing4JUcC89NJL1KxZE4Dw8HD2799v8T42btyoPO7Xr5+tumbRcY09z2qpqalER0dz//59Ll26hI+PD19//TWdOnVi0KBBHDt2LFP7Nfd1REdHywh7kSOsHpmxx9hcfyHEM9evw1tvmV5vb6+tUPLaa9qSq0KIXO/o3KMc+foILaa0oMPMDjndHWElcwIZOjJCQ+SERYsWZdjGw8Mj6zuSxfr168fXX38NaG+kO3bsaPa2ISEhHDmizXXj7OxMt27dsqSPzztz5gy3bt0yWHbkyBEePnxImSz8kmrcuHHUqFFDeR4fH09UVBRBQUGcP38ePz8/UlJSOHnyJKdOnWLAgAFMnjwZezOmMDs4OJCcnMzevXuJiIjI8L21bds21Gq1wbZCZAergxnlJTFhgaVWq7Gzs8ngnnzLfvNmHEeMQBUfb7pRSgqcOoV661ZSLfhPW+RearVaps/lY0fnHjVIDgnQ/IvmOdklkY7UlFSSYpJIjE1UfiZGJ5IUm0RiTCIBGwK4sf2GRfvcP20/SUlJFl133Qd9ITKjQ4eCETTt3r073333HYmJiRw6dIjQ0FBKlixp1rabNm0i9ekI2M6dO+Pm5paVXVXoT2Xp06cPPj4+pKam4uPjw+jRo7PsuA0bNkx3qkxwcDC///47a9euRaPR8Pfff5OamsqMGTMy3HerVq3Yu3cviYmJbNu2jUGDBqXbXjeCo3bt2jx+/JiQkBCLXosQmWWTaiaiYJJpQulITMRxyhQK/fKLWc019vYUmjkTdfv2MjojH9D9XsjvSP5zbO4xjnx9xGDZkVlHQAPNvmiWQ73KP1JTUpUgw/MBiKTopz9162OTSIxOVNrolivrnv5Mjs+abwgtve7yt0CIjHl4eNCxY0d27NhBcnIymzdvZuTIkWZtu2nTJuWxLvdGVouJieHff/8FoHLlykyePJl//vkHtVqNj48PH3zwQY59sVG+fHlmzpxJgwYNmDhxIgCrV6+mSZMmGebzqFGjBo8ePcLf35+NGzemG8y4du0a/v7+gPa8//7777Z7EUJkQIIZItNUKpV882yEKjAQx8GDsTeSoMnkNikp2J89i/2ePTI6Ix9QqVTKyAz5Hck/dFNLjDny9RFQFawRGrrAg0EQIZ0RECbb6T3PqsBDVjky+wjNvzTvmsvfAuuo1bB+PWzeDGFhULw49OoF/fuDs3NO9y53U6vVbNiwgT179nD9+nUiIiJwdXXFy8uLV199lQEDBlC6dOkM96PRaNiyZQubN28mICCAuLg4SpYsSaNGjXjnnXeoU6eOQRLJOXPm0KdPH4v7269fP3bs2AFok1KaE8w4ffo0d+7cAaBixYo0btzY4uNmxs6dO4mLiwOgR48euLm50aFDB7Zv305gYCAnTpygadOm2dIXU3r16sXly5dZvnw5oJ2y1Llz5wxHV/ft2xd/f3+uXLnC5cuXqVWrltF2upEpTk5OdO/eXYIZIltJMENkmrOzMy4uLjndjdxl504YOBDCw7XPdR9ezflGzs4O59mztaVZ5UNvnqcLZsjvSP5wYNYBZUqJKUdmHaFQoUK5MpeCJlWjBBd0AYXEmEQSohMMnifGpG1jallSXFJOvyyjHJwdcHRz1P5zd3z22MiywKOB3Pz3ZsY7NaHNV23M/h1PNZUAWmRo61Ztsa8nT54VBbOzAx8fGDcOli+H7t1zupe504ULF/joo4948OCBwfKIiAgiIiLw9/dn+fLlTJkyJd2EmbGxsXz44YdpEkkGBQURFBTE1q1bmThxIu7u7lb3uVmzZpQvX57g4GBu3brFuXPnqF+/frrb6Ceq7NOnT7YFD3U38iqVip49ewLQu3dvparHhg0bcjyYATBq1CjWrFlDQkIC169fx8/PjwYNGqS7Tbdu3Zg7dy4JCQn4+PgYDWYkJSWxdetWQDsVqkiRIlnSfyFMkWCGELaQnAwzZsDs2c+WVaoE0dHPAhsZSU2FwEBITAQnpyzpphDCctmdHFIJPJgIJGQmAJEnAg9mBiB0/5zcndK2cXXEzsGyXE6WXF99bWa2yZWBq/xm61btCAwdXUxI9zMiAnr21I7Y6NEjmzuXywUEBDBkyBBl5EC1atXo2bMnXl5eREREsGfPHg4fPkx8fDyTJ09Go9HQv3//NPvRaDSMHTtWCWS4uLjQt29fXnrpJQBlKsKcOXPo3Lmz1f1WqVT06dOHBQsWANrRGekFM2JjY5WpHvb29pkaDZIZN27cwM/PD4BGjRrh5eUFQPPmzSldujQhISHs3r2b6OhomwR5rOHp6UmLFi3Yu3cvACdPnswwmFGkSBE6duzI9u3b2bZtGxMmTMDR0dGgzd69e3ny5AmQfVN7hNBnVjBj8ODBymOVSqUMU3p+nTWe368QecbDh/D226BfQqxHD1i2DGJiIDTUoHm8XjLQwoULG+6rVCkJZAiRi2TmRnf/tP2EXQvDu7u30SCEbmqFqaBEbg082DvZpx9IsDAAUci1EPaFMs6qn9V0AQlLrrMEMrKHWq0dkQGmBzhqNNrBjEOHwv37MuVEJzU1lc8//1wJZPTv358ZM2bg4PDso/+AAQNYv349U6dORaPRMHv2bJo1a6bclOv4+PgoVUJKly7NypUrqVSpkrK+V69eDBkyhEGDBilBBWv16dOHRYsWkZqayj///MPkyZNxNnFx9ad6tGjRwqwpM7agn/izd+/eymM7Ozt69uzJ4sWLUavVbNu2jQEDBmRLn9JTv359JZhx8eJFs7bp168f27dvJyIiAl9fX7p06WKwXjciply5cjRrJnmjRPYzK5hx8uRJgzngxtZZQ7L+F2wHZh1g//T9tPkqD3443L9fG8h4+FD73N4e5s6Fzz7TfroqVgwqVDDY5Mg0X72Sji2yv88iy0nZztwvJTGFhKgEk//UkWqu/3OdewfvZWr/F1dd5OIq8z4sZgX9wIPRQIJbIbODEk7uTrkm8JBVLAloSCAj+6xfr51akhGNRttuwwbtTM/8yNvbO931NWvWZMuWLcrz/fv3c+3aNWXbr776ymhJzv79++Pv78+aNWuIj49nxYoVTJo0yaDNsmXLlMfffPONQSBDp0KFCsyZM4ehuuiTlcqVK0fz5s05fPiwkmSzl/4QHT36U0zSmypjS0lJScr5Lly4cJoRKb169WLx4sVK/3JDMKNcuXLK43AzRw03bdoULy8vgoKC2Lhxo0EwIyQkhMOHDwPaYI5UOBQ5wexpJull4ZYM3SKz9L/1tMXw7GyTmgrffgtTpjwb61quHKxdC6++anIz/Xn3uXl+vcg8ucZZR6PRkJKQcRBC9zgxKtFkm5SElJx+OQp7R3uTwQQndycl8GByVMTz27k6Yu+YfwMPWcWcgIYEMrTWr4dp07QzKbNSWJhl7UeOhC++yJq+6Li7w6xZkE33zJm2e/du5fHw4cONBjJ03nvvPaV85+7duw2CGYGBgUpQpFq1aryazmecZs2aUaNGDaW9tfr166fcLPv4+BgNZty+fZuzZ88CUKxYMdq1a2eTY2dk7969SkCgY8eOuLq6GqyvWrUqdevW5cKFC/j7+xMQEEDNmjWzpW+m6OeziIiIMGsblUpF7969WbBgAUePHuXhw4eUKVMGgM2bN5OSkqK0ESInmBXMWLFiRabWCZEeY8O380RAIywMBg+Gf/55tqxDB/j7b+00ERPy7OsVZpNrbJxGoyE5PtkmQYjUpLyTRLFq56rU7F0z/QCEBB5ylfQCGhLIeGbePAgIyOlepKVWQ3Bw1h9n3rzsD2YsWrQo3fVubm4Gz8+fP688btEi/VGg5cuXp0qVKty8eZP79+/z6NEjSj39PKM/HaFJkyYZ9rNJkyY2C2a0b98eDw8PIiIiOHnyJIGBgVR4brSrj4+P8rhnz54UKlTIJsfOiP5oEFM38r169eLChQuAdkrKlClTsqVvpuh/+WzJiHj9KT+bNm3igw8+AJ6d+8aNG6e5LkJkF7OCGemVN8qu0kcif0lvHnquvvk7cQLeeAPuPR16rlLB9OnaERrpfOuRZ1+vMFt+vMYajYak2CSrgxAJUQmkJudsEEJlr8KpiJPyz7mos/LYsYijwTr9NpfXX+bM4jMWH09ufPMuYwENuZ6GJkyAqVOzZ2SGWm1+e2dnbcnWrOTuDp9/nrXHMKZDB8umLIY+zdfl6upKyZIlM2xfuXJlbt68qWyrC2Y8evRIaVOxYsUM95PeTe39+/e5fPmyyfVly5aldu3aynNHR0d69OjBihUr0Gg0bNq0iY8++khZn5KSwubNm5Xn2TXFRH96RZkyZUxWK+natStz5swhKSnJaALN8PBwZVSJMR4eHrzyyis263dUVJTBvs2ly4dx5MgRJZihXwpXEn+KnCTVTES2MyehXq67+dNoYMECGD8ekp4m5ytRAv73P+jYMd1N8+TrFRbJbddYk6rRJphMJwihH4hILwihSc3ZaYR2DnY4FbU8CPH8MofCDpnKzVSlQxXcvdwlOWQB03pqa5KSkpTcN3I9DfXrlz0jE1au1A6ENNeSJfk3Z4alYmNjAcwuHazfTrctoCTWBEwm4DS1n+cdP36cL7/80uT63r17M3fuXINl/fr1U0aBb968mTFjxii5GQ4dOqQEW+rWrUv16tUz7J8t+Pj4kJKinarYo0cPk7kiPDw8aNeuHbt27TKaQPP69et8+OGHJo/TuHFjVq5cabN+B+sNW/L09LRo2759+3LkyBHu3r3LqVOnlFEZ7u7uNqlgI0RmSTBDZKvsLnFoE1FRMGKEdpKwTosW2vwY5cunu2mefL3CIra8xqkpqSRG2yAIEZ0AOZzKyN7JPlMjIZ5fZu9kn+MJoiU5ZMHU/IvmNJvYLMfffwVZ//4wbpy2/Gp66dlUKvDwyP15LLKTq6srUVFRBsGI9Oi308//oB+cUJsxTMbc45nL29ubOnXqcPHiRYKDgzl+/DjNmzcHDKeYZNeoDI1GYzDFZPHixUqiz4w8n0Azu+nKyII2+GOJjh07UrRoUSIjI1m5ciWHDh0CoEuXLmYFuYTIKhLMEJmmVqstylx8dO5RJTGiufZP28/xn45T2PO5EqYmPlua/NCZzmfRdLdRq1EFBaNKKgmMBkBTvDiEl4KOm9M9RlxoHHGhlv2nvn/afk7/dhr38u6geto3FajsVM8eZ/TTWFu7DNqSftvnl5larlKp0rRJt62dGW11fbMz77Wmdw5UWNDWjNd6ZcMVrqy5YvE1vvC/C7iXddcGLqKfBiWiE0iKyfmSnA6FHXB01wYbHN2f5noo+vRnkac/3R2fPS6i1/bpT0d3RxycrP/vJTE1EeIzbpcdGn3WSPtNfTp/w1pMbUGjzxrZ/MO8yBlqtdqm1dbMuREUhpydYfly6NlTG7AwFtDQXZ7ly6Usq76SJUsSFRVFbGwsjx8/pkSJEum2100ZAJQpJs8/vncv4+pOgYGBJtf16dOHPn36ZLiP5/Xr10/J3bFx40aaN29OeHi4Uma0cOHCdO3a1eL9ZsaJEyfSfY3pOXr0KA8ePKBs2bKANr/I1atXbdk9k8LCwpTyumB5mgBHR0e6devG33//za5du5Tl2RVEEsIUCWaITNNoNGZXsjk29xhHvrYskKGjDlejDs/JD4Eehk/DsDzFugVi7scQcz8my/Yvcl54QDjhAeaVRTNXIddCz4IPRRyfBReMBSD0HjsVfRaAcHR3tFn5zfxY5arZxGagwejfshZTWtBsYrN8+boLKt21tOT/OnP2JyzTvTts3gxDh2rLr9rZaYuI6X56eGgDGd2753BHc5mXX35ZyYFx+PBhk2VNQZvL4tatW4A2P4J+jo06deooj0+cOJHhcc1pY6lu3boxd+5c4uPj8fX1JTo6mq1bt5L0dNpv586d0yRAzSobNmxQHnfu3NmsqS3nzp3jyJEjpKam4uPjk+7Ukqzy22+/kZiYCGhHu7z88ssW76Nv3778/fffyvPq1atbPMJDCFszK5iR3vw2W1GpVHzzzTdZfhxhO8q38GY4MjtzgQx9zp5Pv3Ix8XnQ5AfFdD4/Gt1GgzbjWJLet+R2dlC4MBpTr/e53STF5vw37CJv0AUR9Ec1OBV1Mj4CQn+ZbsrG05Kcdg5S3z07NP+yOagwGKHRYmoLmn/RPAd7JbKCSqVSRmbYYnSGTFfJvB494P592LABNm2C8HDw9ITevbVTS2RERlqdOnVSpmEsXbqU7t27myzPumTJEuXzUKdOnQzWVahQQSm3euPGDQ4fPmyyPOuxY8dsVslEn5ubG507d2bz5s2o1Wq2b99uMMUkuxJQRkVF8d9//wHg4ODAjBkzzMo9ERAQQM+ePQHt1JjRo0dn69+DzZs3G1SfHDNmTKaOX7t2bV577TUePHgAwBtvvGGzPgqRWWYFMzZt2pQtv3QSzMhbnJ2dzU4s1earNhYl0EuzfXbNQ796VfvJyN//2bIPPoD/+z+LPi1ZkkfBmDYz29BqSivQPP1WMFWjPDb4marJ+mUmjm/rZXmt77f33ebO3juZvsYtJ7ek7cy22ukrIk/pMFNbVUCXHFL3XOQ/umCGuf/XpSc1Ne+UFc6NnJ21yT0lwad5WrdurQQhAgICmDFjBtOnT8fBwfCjv4+PD2vWrAG00zUGG8m4OnToUCZNmgTApEmTWLlyJZUqVTJoExgYmKVffvbr10+pXPLLL78oiT8rVapEo0aNsuy4+rZt20ZCQgIALVu2NDuJZs2aNXnxxRe5cuUKQUFBHD9+nGbNmmVlVwHtiJvffvuNtWvXKssGDhyYJmBlifnz59uia0LYjNnTTCwZHqn7NsOS9fKNRf5mSQK952VbIGPtWm2iz5inUzxcXWHxYhgwwOJd2ez1qtDmebDNyH9hQ62mtMp00EqSROZ9khxSCJGb2dnZMW/ePN5++23i4uJYt24dfn5+9OjRg/LlyxMZGcmePXuURI4AkydPpryRxOZ9+vRhx44dHDlyhJCQEHr16kXfvn2VKSgXL15k48aNxMfH89prr/Hvv/8qfbCVRo0aUblyZe7cuWNQLrZPnz6Z+ju8YcMGjh49albb0aNH4+TkZJD4M71pO8b06tWLK1euKMe2RTDjzJkzROvVR1ar1URHRxMYGMj58+c5d+6cUnVFpVIxcOBAJSglRH5hVjBjzpw5GbbRRf9089fq1atH/fr1KVu2LIULFyY+Pp4HDx7g5+enZNN1dHTk/fffp1y5cpl/BSLPyMwNfrbc9CUkwKefwi+/PFtWu7a2esmLL2Z6t7n29QqbkWsshBAit6pZsybLly9n7NixPHz4kGvXrvH999+naVe4cGEmT55M//79je5HpVKxYMECRo8ezfHjx4mLi0tTMtTe3p4vvvgCV1dXJZihXxXFFvr27csPP/xgcMzevXtnal9bt241u+27777LrVu3uHTpEgBFixalXbt2Fh2ve/fuzJs3j+TkZHbv3k1UVBRFihSxaB/PM2eUhEqlolGjRnz44Yc0bdrUquMJkRuZFczI6A/F+fPnmTt3LsnJybRo0YKpU6dSuXJlk+3v3r3L119/zaFDh1ixYgWLFy/OVCIakffkuhKHd+5oa7+dPv1s2aBB8Ouv2pEZVsp1r1fYnFxjIYQQuVXdunXZtWsX69evZ8+ePVy/fp3IyEhcXFzw8vKiZcuWDBgwgNKlS6e7H1dXV5YtW8aWLVvYtGkTAQEBxMXFUbJkSRo1asTAgQOpU6eOQZnSokWL2vS19OrVi59++kkZbfDqq69m2G9b0U/8+frrr+Po6GjR9sWLF6dly5bs27ePhIQEtm3bxjvvvGOz/tnZ2eHi4oKbmxuenp54e3tTu3ZtWrduTcWKFW12HCFyG5XGyvTakZGR9OzZk5CQELp06cL3339v1nAvjUbD+PHj2bFjB2XKlGHz5s14eHhY0xWRxWJiYgxKSHl7e2c6e3RGw/Oz5aZv2zYYPFhbwB7AyQkWLoR3331W681GcsXrFVlKrnHBEhcXZ9N8CiL3sfU1tuX/obZ2/fp1kpOTcXBwMKs6gxCmjB07VkmSefLkSZsHNIQQBYO5/y9ZPZlt/fr1PHz4kMKFC/PVV1+ZPW9NpVLx1Vdf4eLiQkhICOvWrbO2KyIPaT21NW1mtjG6Lstv+pKTYeJEbXp0XSCjalU4flybMyML5sDn6OsV2UKusRBCiIIsKCiIffv2AfDiiy9KIEMIkeWsDmbs2rULlUpF06ZNLf6Gwc3NjaZNm6LRaJQorig4jN38ZflNX3AwtGsH3333bFmfPnDmDNSrl3XHJYder8hWco2FEELkRzdu3CA8PNzk+ocPHzJmzBgld97bb7+dXV0TQhRgZlczMSUoKAiAEiVKZGp73XbBwcHWdkXkQUq+gen7afNVFt/0+fpqK5OEhmqfOzjA99/DRx9lyWgMY1pPbU1SUpJS0lFucvMfucZCCCHymwMHDvDjjz/StGlTGjRogJeXF46Ojjx58oTz58/z77//Eh8fD0CDBg3o169fDvdYCFEQWB3MiIuLAyBUd4NoId12uv2Igqf11NZZe8OXmgpffw0zZoAuRUyFCrBuHeRAZmcp6Zj/yTUWQgiR3yQlJXHo0CGDcq7Pa968OfPnz8feXmrKCyGyntXBjJIlSxIUFMTx48eJjo7G3d3d7G2jo6M5fvw4KpWKkiVLWtsVIdIKDYWBA0F/GtPrr8OKFZDJ0URCCCGEEAVJ7969cXJy4tixY9y5c4eIiAgiIyNxdHSkRIkS1KtXj65du9K6tYxGFEJkH6uDGc2aNWP9+vWo1WqmTZvG//3f/5n9beT06dOJj49Xcm4IYVNHjsCbb2rzZADY2cHMmfDll9rHQgghhBAiQ56engwcOJCBAwfmdFeEEEJh9R3d22+/rQwl+/fffxkxYgS3bt1Kd5vbt28zYsQIdu7cqe2EnR0DBgywtitCaGk08MMP0KbNs0BG6dLanBmTJ0sgQwghhBBCCCHyOKtHZtSqVYsRI0bw+++/o1KpOHr0KF27dqV27drUq1ePcuXK4ezsjFqt5v79+5w/fx5/f38ANE/zF4wYMYJatWpZ2xUhtKVWhw2DzZufLWvdGlavhrJlc6pXQgghhBBCCCFsyOpgBsAnn3yCRqNhyZIlSoDi0qVLXLp0yWh7XRuVSsXw4cP55JNPbNENkc3UajV2uWiUg+rcOZwGDcLu9m1lWdL48SRNnaqtXJJLksyq1Wo0Go0kh8zH5Brnf3KN8z9bX2O1Wm2T/QghhBBCyybBDIBPP/2Uli1b8sMPP+Dn56cELEypX78+n3zyCY0bN7ZVF0Q202g0GV7nbOoIDn/9heOECagSErSLihUjYckSUl57TWmTW+jOWa45f8Lm5Brnf3KN8z9bX2N5nwghhBC2ZbNgBkCjRo1Ys2YNN2/e5MSJE1y5coXw8HDi4uJwcXHB09OTF198kSZNmlC1alVbHlrkAJVKlfPfSsbE4Dh2LA7r1imLUl55hcSVK9FUrEhu/M5UpVIp3/bl+PkTWUKucf4n1zj/s/U1lveJEEIIYVs2DWboVK1aVYIVBYCzszMuLi4514HLl6FfP7hy5dmysWOx//57Cjs65ly/zKD7gJyj509kKbnG+Z9c4/zPltc4NTXVBj0SQgghhE7uSXgghCVWrYJGjZ4FMtzdYd06+PlnyOWBDCGEEEIIIYQQ1smSkRlCZBm1GsaNg8WLny2rWxfWr4caNXKuX0IIIYQQQgghso0EM0TecfOmdlqJn9+zZcOHw8KFULhwjnVLCCGEEEIIIUT2ypJgRmxsLAEBATx58oTY2FizM3j36tUrK7oj8gMfHxg2DKKitM8LF4ZffoGhQ3O0W0IIIYQQQgghsp9Ngxnbtm1j1apVXLx40eISZCqVSoIZIq3ERPjiC/jxx2fLatSADRugTp2c65cQQgghhBBCiBxjk2CGWq3m448/5sCBA0D6tdR1pc6EyFBgILz5Jhw79mzZm2/CkiXahJ9CCCGEEEIIIQokmwQzJk+ezP79+wFwcnKiSZMmBAUFcevWLWXERWxsLMHBwVy9epXk5GRUKhWFCxemU6dOUnu9oPP1hY8+0lYi6dBBu+zff2HgQAgL0z4vVEg7OmP0aJD3ixBCCCGEEEIUaFYHM86fP8+OHTtQqVRUrFiRv/76i/LlyzNr1ixu3boFwJw5c5T2MTExrFu3jkWLFhEXF0dYWBg//vgjbm5u1nZF5EUaDUyapC2xOmkStGkDX30Fs2dr1wFUqqStVtKoUY52VQghhBBCCCFE7mBn7Q42bdqkPP7mm28oX758uu3d3NwYPnw4GzdupGTJkhw+fJhJkyZZ2w2RV/33H5w6pX186hS88gp8/fWzQEa3bnD2rAQyhBBCCCGEEEIorA5mnDlzBoCKFSvSsGFDs7erXLky3377LRqNht27dyvTVEQBotHA1Klgb/9s2fnz2p/29vDtt7BlC3h65kz/hBBCCCGEEDkiKCgIb29vvL29+eKLL3K6O1nKx8dHea0+Pj453Z08w+ppJo8ePUKlUvHiiy8aLNfPg5GYmIijo2OabZs1a0b16tW5ceMGW7dupU2bNtZ2R+Ql+qMy9Hl6wqZN0KpV9vdJCCGEECIX8Pb2tqh948aNWblyZRb1Juv5+/vTt29fADw9PTl48CCFChWyaB87d+7k448/BqBOnTps2LBBWTdo0CBOnjwJwIoVK2jSpIltOg788ccfzJs3T3n+008/8frrr9ts/zr6r0GfnZ0drq6uuLu7U6xYMby9valVqxatW7emYsWKZu3bx8eHL7/80mDZkiVLaGXm5/HPPvuM7du3Gyy7evWqWdsKkVlWj8yIjY0FwMPDw2C5k5OT8jgmJsbk9rVq1UKj0XDp0iVruyLyEt2oDDsjb8GKFaFly+zvkxBCCCGEyBEvvfQSNWvWBCA8PDxTo7Y3btyoPO7Xr5+tumbRcY09z2qpqalER0dz//59Ll26hI+PD19//TWdOnVi0KBBHNOvDGgBc19HdHQ0vr6+mTqGENawemSGs7MzsbGxJCcnGywvUqSI8vj+/ft4mpgqoCvT+ujRI2u7IvISU6MyAPz8tOs7d87WLgkhhBBC5EaLFi3KsM3zXyzmRf369ePrr78GtDfSHTt2NHvbkJAQjhw5AmjvT7p165YlfXzemTNnlKIHOkeOHOHhw4eUKVMmy447btw4atSooTyPj48nKiqKoKAgzp8/j5+fHykpKZw8eZJTp04xYMAAJk+ejL3+9G4THBwcSE5OZu/evURERGT43tq2bRtqtdpgWyGyg9XBjLJly3Ljxg0iIiIMlleuXFl57Ofnx0svvWR0+xs3bljbBZHX6OfKSElJu97eXru+UycpwyqEEEKIAq+DrnR9Pte9e3e+++47EhMTOXToEKGhoZQsWdKsbTdt2kRqaioAnTt3zrZKifpTWfr06YOPjw+pqan4+PgwevToLDtuw4YN050qExwczO+//87atWvRaDT8/fffpKamMmPGjAz33apVK/bu3UtiYiLbtm1j0KBB6bbXjeCoXbs2jx8/JiQkxKLXIkRmWT3NpEaNGmg0Gm7fvm2wvG7dukrejLVr1xqN0B0+fJjLly+jUqmoUKGCtV0ReYVuVIaxQAZol586pW0nhBBCCCEKBA8PD2U0RnJyMps3bzZ7W/0Ki7rcG1ktJiaGf//9F9B+kTt58mScnZ0BbQ4K3Qj0nFC+fHlmzpzJt99+qyxbvXo1O3fuzHDbGjVqKF9EZzTV5Nq1a/j7+wPZd96F0LF6ZMYrr7zCjh07uH37tsEwpLJly9KwYUNOnz7NjRs3GD16NB9//DHVq1dHrVazZ88e5s6dq+ynbdu21nZF5AUZjcrQkdEZQgghhNBJUcO99RC0GRLCwKk4ePWCiv3B3jmne5erqdVqNmzYwJ49e7h+/ToRERG4urri5eXFq6++yoABAyhdunSG+9FoNGzZsoXNmzcTEBBAXFwcJUuWpFGjRrzzzjvUqVPHIInknDlz6NOnj8X97devHzt27AC0AYGRI0dmuM3p06e5c+cOoK2w2LhxY4uPmxk7d+4kLi4OgB49euDm5kaHDh3Yvn07gYGBnDhxgqZNm2ZLX0zp1asXly9fZvny5YB2ylLnzp2xM5a3Tk/fvn3x9/fnypUrXL58mVq1ahltpxuZ4uTkRPfu3fn9999t+wLMlJKSwtatW9m1axeXL1/myZMnODs7U6ZMGZo3b85bb73FCy+8kO4+UlNT2bFjBzt37uTKlSuEhYWh0Wjw8PCgWLFiVK5cmSZNmtClSxeKFSuWZvvExER8fHzw9fXl6tWrREREYGdnR7FixShWrBhVq1alefPmdO7cGVdXV6te7969e/n33385d+4cjx8/JjU1leLFi9OgQQP69OlD8+bN881rTY/VwYzWrVujUqnQaDTs37+fXr16Kes+++wzBgwYAMChQ4c4dOiQ0X0UK1aMIUOGWNsVkc3UanWGfwifZ7d7N86mcmXoezo6Q711K6kWzJfMC9RqNRqNxqDij8hf5Brnf3KN8z9bX2PdfHKRCUFb4dhQSHqCdlBxqvZnoA+cHgfNloNX95ztYy514cIFPvroIx48eGCwPCIigoiICPz9/Vm+fDlTpkxJN2FmbGwsH374YZpEkkFBQQQFBbF161YmTpyIu7u71X1u1qwZ5cuXJzg4mFu3bnHu3Dnq16+f7jb6owf69OmTbX+bdTfyKpWKnj17AtC7d2+lqseGDRtyPJgBMGrUKNasWUNCQgLXr1/Hz8+PBg0apLtNt27dmDt3LgkJCfj4+BgNZiQlJbF161ZAOxVKP2didrp37x6jR4/m+vXrBssTExOJiori2rVrrFq1ig8//NDk1J8nT54watQo/Pz80qx79OgRjx494urVq+zatQu1Ws27775r0CYwMJARI0YoQTV9Dx484MGDB1y+fJlt27bh4uLCa6+9lqnX+uDBAz755BPOnTuXZl1wcDDBwcFs27aNzp078+2331K4cOE8+1rNYXUwo1y5cgwdOpSQkBDCw8MN1tWvX59Zs2YxY8YMk4lgPD09+eWXX0wmCBW5l0ajsWz4nEZDoZkz0djZoXo6pzHd5nZ2FJo5E3X79vlqdIbunFl8/kSeIdc4/5NrnP/Z+hrL+ySTgrbCwV56C1INfyZFwMGe0GozePXI1q7ldgEBAQwZMkQZOVCtWjV69uyJl5cXERER7Nmzh8OHDxMfH8/kyZPRaDT0798/zX40Gg1jx45VAhkuLi707dtXmYbg7+/Pxo0bmTNnDp1tkLxdpVLRp08fFixYAGhHZ6QXzIiNjVWmetjb22dqNEhm3LhxQ7kZbNSoEV5eXgA0b96c0qVLExISwu7du4mOjrZJkMcanp6etGjRgr179wJw8uTJDIMZRYoUoWPHjmzfvp1t27YxYcIEHB0dDdrs3buXJ0+eADk3xSQkJIS3336bx48fA9rpNb1796ZKlSrExcVx6NAh/vvvP5KTk5k/fz6JiYlK+V59U6dOVa5n2bJl6dKlC5UrV6ZIkSLEx8dz584d/Pz8OHPmjNF+jBs3Trm5r1KlCq+99hrlypXD3d2dmJgYbt++zenTp7lw4UKmX+uDBw/o378/oaGhgLYqaPv27alUqRJ2dnbcvn2bzZs3ExgYyK5du4iLi2PJkiVpgnt54bWay+pgBsDEiRNNruvXrx/169dn2bJlHD9+nEePHmFnZ4eXlxft2rVjyJAhEsjIo1QqlWWR78REVEFBZgUyAFSpqaiCg1ElJYFeqd+8TjeSyeLzJ/IMucb5n1zj/M/W11jeJ5mQotaOyADAVDBIA6jg+FDofV+mnDyVmprK559/rgQy+vfvz4wZM3BwePbRf8CAAaxfv56pU6ei0WiYPXs2zZo1U27KdXx8fJQqIaVLl2blypVUqlRJWd+rVy+GDBnCoEGDlKCCtfr06cOiRYtITU3ln3/+MchF8Tz9qR4tWrQwa8qMLegn/uzdu7fy2M7Ojp49e7J48WLUajXbtm1TRqrnpPr16yvBjIsXL5q1Tb9+/di+fTsRERH4+vrSpUsXg/W6ETHlypWjWbNmtu2wmaZOnaoEMlq3bs38+fMNRiP079+fAwcOMGbMGBITE/n9999p06YN9erVU9qEhYWxZ88eQHueli9fjpOJe4/w8HAlgKNz8eJFLl26BMBrr73Gjz/+aHL0enBwcKaC2xqNhk8++YTQ0FDs7e2ZMWMGb7zxRpp27733Hl988QU7duzg0KFDbNiwwSBImRdeqyVsEszISNWqVZk1a1Z2HEpkI2dnZ1xcXMzfwMUFTp+Gp9FEc9iVKoWLkXlaeZ3uA7JF50/kKXKN8z+5xvmfLa9xqpmBfKHn3vqnU0syooHEJ3BvA7wwMMu7lRO8vb3TXV+zZk22bNmiPN+/fz/Xrl1Ttv3qq6+MluTs378//v7+rFmzhvj4eFasWMGkSZMM2ixbtkx5/M033xgEMnQqVKjAnDlzGDp0qAWvyrRy5crRvHlzDh8+rCTZ1J/Krk9/ikl6U2VsKSkpSTnfhQsXTjMipVevXixevFjpX24IZpQrV055/PxoelOaNm2Kl5cXQUFBbNy40SCYERISwuHDhwFtMMfSqee2cPXqVQ4cOABAyZIl+b//+z+j0ypat27N2LFj+eGHH0hNTWXJkiUG5Y4DAwOVv9Hdu3c3eXMP2lEuz38Rf+/ePeVxnz590j0X5cuXN+/FPWfv3r3K1JIxY8YYDWQAODo6MnfuXPz8/AgODuavv/4yCGbkhddqiWwJZgihqFBB+08IIYQQedO99XBhGiRFZ+1xEsIsa39iJPh9kTV90SnkDnVnQcXsuWnOrN27dyuPhw8fbjSQofPee+8p5Tt3795tEMwIDAxUgiLVqlXj1VdfNbmfZs2aUaNGDaW9tfr166fcLPv4+BgNZty+fZuzZ88C2hx87dq1s8mxM7J3714lINCxY8c0CQ6rVq1K3bp1uXDhAv7+/gQEBFCzZs1s6Zsp+vksIiIizNpGpVLRu3dvFixYwNGjR3n48CFlypQBYPPmzaSkpChtcoL++/ytt95KtxzvwIED+e2334iNjeXAgQMkJCQoN/L6ARDdqANL6G/v7+9P69atLd5HRnSVfRwdHRk8eHC6bR0dHenWrRu///47t27d4v79+0owKy+8VktIMEMIIYQQQpjv8jyICsjpXqSVqob44Kw9RjxwZV62BzP0v0U25vmbuPPnzyuPW7Roke625cuXp0qVKty8eZP79+/z6NEjSpUqBRhOR2jSpEmG/WzSpInNghnt27fHw8ODiIgITp48SWBgIBWe+0LMx8dHedyzZ08KFSpkk2NnRH80iKkb+V69eik5AzZs2MCUKVOypW+m6A/3t2Tam/6Un02bNvHBBx8Az85948aN01yX7KL/Pk8v0AbaXC8NGzbk4MGDJCUlcfnyZSUXS7Vq1ZQ8Jxs3biQ1NZX+/ftTr169dAOBOg0aNKBw4cLEx8fzyy+/EBERQe/evXnxxRdtNsXw1NMCCiVKlOD48eMZto+MjFQe37hxQwlm5IXXaoksD2bExsYSGxuLq6trlpZlEUIIIYQQ2aDWBLgwNXtGZqRaUAXGzllbsjUrFXKHFz/P2mMY0aFDB4va6xIEurq6UrJkyQzbV65cmZs3byrb6oIZjx49UtpUrFgxw/2kd1N7//59Ll++bHJ92bJlqV27tvLc0dGRHj16sGLFCjQaDZs2beKjjz5S1qekpCjfVkP2TTHRn15RpkwZk9VKunbtypw5c0hKSjKaQDM8PFwZVWKMh4cHr7zyis36HRUVZbBvc+nyYRw5ckQJZuiXwrUk8efNmze5ffu2yfUvvPACVatWNXt/oXpT1ytXrpxh+8qVK3Pw4ME029rb2zNr1iwlr8amTZvYtGkTbm5uvPzyyzRo0IBmzZrRoEEDozfsHh4eTJ48mWnTppGcnMyKFStYsWIFHh4e1K9fnwYNGvDqq6+aLG+bkbi4OCV3xf379/nwww8t2l4/sJHbX6ulbB7MCA4OZt26dZw4cYLLly+TlJSkrCtUqBC1atWiSZMmvPnmmwZzt4QQQgghRB5QsV/2jEy4vRKOpT+c2kCTJfk2Z4alYmNjAczO96LfTrctoCTWBEwm4DS1n+cdP36cL7/80uT63r17M3fuXINl/fr1Y8WKFYB2mP2YMWOUOfqHDh1Sgi1169alevXqGfbPFnx8fEhJSQGgR48eJnMGeHh40K5dO3bt2mU0geb169fTvSlt3LgxK1eutFm/g4OfjVqytPhC3759OXLkCHfv3uXUqVPKqAx3d3eLKtj8888/LFy40OT6MWPGMHbsWLP3p/9eNee9bup9Dtq8Ghs3bmThwoXs3buXpKQkYmJiOHLkCEeOHGHBggV4eXnx0UcfKWV49fXv358XXniBX3/9laNHj5KamkpERAT79u1j3759/PDDD9SoUYPx48dbPDUjOtq6wLH+/Xhuf62WslkwIzExkXnz5vG///1PSSryfPbSxMREzp8/z/nz5/njjz945513GD9+fJoyP0IIIYQQooCr2B9Oj9OWXzVZzQRABY4euT6PRXZydXUlKirKIBiRHv12+iOp9W/+1OqMR8mYezxzeXt7U6dOHS5evEhwcDDHjx+nefPmgOEUk+walaHRaAymmCxevFhJ9JmR5xNoZjddKU7QBn8s0bFjR4oWLUpkZCQrV67k0KFDAHTp0sWsIFdW0X+vxsXFZXhPaep9rlOjRg1+/vln4uLiOHv2rFKe9PTp0yQmJhIUFMSECRMIDAxkzJgxabZ/5ZVX+PPPP4mMjOTMmTP4+flx+vRpzp8/T3JyMteuXeO9995jzpw5FpUQ1v89rF27tsF7P7Ny62u1lE2CGWq1mmHDhuHn55dh+RXd+pSUFFauXMnFixdZtmxZuplUhRBCCCFEAWPvDM2Ww8GegArjAY2nw6CbLpeyrHpKlixJVFQUsbGxPH78mBIlSqTbXjdlAFCmmDz/WL+KgSmBgYEm1/Xp0ydTNzX9+vVTcnds3LiR5s2bEx4erpQZLVy4MF27drV4v5lx4sSJdF9jeo4ePcqDBw8oW7YsoM0vcvXqVVt2z6SwsDClvC5oR31YQpdQ8u+//2bXrl3KckuDSGPHjrVo5EVGSpYsyZUrVwC4e/duhtNnTL3Pn+fi4sKrr76q5OGIiYlhxYoVzJ8/H4DffvuNN9980+QUrqJFi9KuXTslIW14eDiLFi1i1apVAHz77bd0797d7Bwv7u7uuLi4EBcXx8OHD83axly57bVayiY1dKZMmaKUigGoXr06X3zxBevWrePIkSOcPXuWI0eOsG7dOr744gtq1KgBaAMbfn5+OZ4QRwghhBBC5EJe3aHVZu3IC+DZR9enPx09oNUWbTuhePnll5XHuvwOpty/f59bt24B2vwI+jctderUUR6fOHEiw+Oa08ZS3bp1Uyoo+Pr6Eh0dzdatW5Wh8507d063ioUtbdiwQXncuXNnxowZk+E/XQLW1NRUm3yjnhm//fYbiYmJgHa0i/77w1zP58aoXr26xSM8bM2S93l8fDxnzpwBnqU+MJebmxujR4+mffv2gHbahn7y0Yx4enoydepUpaJNREQEN27cMHt7eBaACgsLw9/f36JtLZEbXqslrB6ZceHCBbZv345KpcLOzo7PP/+cIUOGpEkY4uLiQvHixalbty5Dhgxh5cqVfPvtt6SkpLB9+3YGDRqU478QQgghhBAil/HqAb3vw70NELQJEsLByRO8emunlsiIjDQ6deqk3DgvXbqU7t27m6xUsGTJEmXkdKdOnQzWVahQQSm3euPGDQ4fPmyyasSxY8dsVslEn5ubG507d2bz5s2o1Wq2b99uEBSwJAGlNaKiovjvv/8AcHBwYMaMGWblnggICFDyDvj4+DB69OhsrfqwefNmJe8IaPNSZOb4tWvX5rXXXuPBgwcAvPHGGzbrY2Z16tSJBQsWALB69WqGDBliMrD1999/K3ky2rRpk6k0B15eXsrj5OTkTG0fEBCQqe179erF/v37Afjpp59YsmRJlr6PcvK1WsLqkRlbtmxRHn/++ecMHTo0wxOrUqkYPHgwEyZMMLofIYQQQgghFPbO2uSeLTdCh33any8MlECGCa1bt1ZGQgcEBDBjxgyjNxQ+Pj6sWbMG0E7XGDw4bcLVoUOHKo8nTZrE3bt307QJDAxMN7mntfSnM/zyyy/K9IxKlSrRqFGjLDuuvm3btpGQkABAy5YtzU6iWbNmTV588UUAgoKCzCqraQv3799n2rRpTJw4UVk2cODANAErS8yfP59169axbt26bMtTkp4aNWrQpk0bQFud5LPPPiM+Pj5Nu0OHDvHzzz8DYGdnx8iRI9OsX7ZsmUHVj+eFhYUpwSxAGXkAsHXr1v9n777DoyrzNo5/z6Q3CEnoEHroSBVRAQs2pAus7lqwu4q79teuqKuuDV1Z17VjWxWkF7ECFgTpPaFDIJSQXiZl5rx/DJkkJJBJZiaTDPfnurhmcuov8yQkuecpzJgx47RzxuzZs4cVK1YAEBISQrt27ar+BMu4/PLLnT1Rfv75Zx566KEKk5iWZbPZWL58OW+99Va57fXhc60Ot3tmrFq1CnCMOyr7n50rrr/+ej744AOOHj3qlW5pIiIiIiJnGovFwssvv8w111xDXl4eX331FevXr2fUqFG0bNmSzMxMfvjhB+dEjgCPPfYYLVu2rHCtcePGsXDhQn799VeOHDnCmDFjuOqqq5xDUDZt2sTXX39Nfn4+l19+Od98842zBk8ZMGAAbdu2Ze/eveWWix03blyN3p2eOXMmv/32m0vH3nnnnYSEhJSb+HPMmDHVut+YMWOcczvMnDmTQYMGVev8yqxZs6bcKhdWq5Xs7GwOHDjAhg0bWLdunXPVFcMwuPbaa3n00Ufdvm9d88wzzzBu3DhSU1NZunQpV155JePGjaN9+/bk5uby66+/8s033zh7H91xxx0VhtkcO3aMF154gVdeeYWzzz6bs846i9atWxMeHk5GRgaJiYksXLjQGQBcccUV5ZaC3bdvH9OmTeMf//gHgwYNomfPnrRo0YKQkBDS0tLYtGkTS5YscQYA1113XbWHRhmGwZtvvsmf/vQnUlJSmDdvHsuWLePyyy+ne/fuNGzYkIKCAo4ePcr27dv57bffSEtLY9CgQdx555316nOtDrfDjCNHjmAYRo3WQS45b+HCheX+YxIRERERkZrr0qUL06dP5+677+bw4cMkJSXxyiuvVDguLCyMxx57jAkTJlR6nZI/ou68805+//138vLyKiwZGhAQwMMPP0xERIQzzKhstQh3XHXVVbz66qvl7jl27NgaXWvevHkuH3vzzTeze/dutmzZApROeFgdI0eO5OWXX6a4uJjvvvuOrKwsGjRoUK1rnKxkgsbTMQyDAQMGcNddd3HOOee4db+6qmnTpnz++efceeed7Ny5k4MHDzqHnpQVGBjInXfeWelyuCWBWFFRkXN50lO57LLLeOGFFyo9Pz8/nx9//NE5OW1l9/nzn//Mfffd5/LnV1bTpk35+uuvefjhh1m+fDmZmZl8+eWXpz2nWbNmldZa1z9XV7kdZpQs0+TqOtYnKznPleWeRERERETENb169WLJkiXMmDGDH374gR07dpCZmUl4eDitWrVi8ODB/PnPf6Zp06anvU5ERAQfffQRc+fOZfbs2Wzfvp28vDwaN27MgAEDuPbaa+nZs2e5ZUobNmzo0c9lzJgxvP76687eBueff36VdXtK2Yk/r7jiimrPtxAbG8vgwYP56aefKCgoYP78+fzlL3/xWH0Wi4Xw8HAiIyOJiYmhc+fOdO/enaFDhxIfH++x+9RVbdq0Ye7cucybN49vv/2WLVu2kJ6eTmhoKM2bN2fQoEFcc801pxzuMGbMGDp06MCKFSvYsGEDu3bt4ujRoxQUFBAaGkqLFi0466yzGD16dKUrwdxxxx0MHDiQ33//nY0bN7Jnzx6OHTtGUVER4eHhtG7dmr59+3LVVVdVa+LRysTGxvLuu++yfv165s+fz5o1a0hJSSE7O5uQkBDi4uLo0KEDffv25cILL6RTp0719nN1hWFWtZZqFYYOHcrRo0cZMGBAucllXHXDDTewcuVKmjZtyrJly9wpRbwsJyen3BJSnTt3rrXZo/1JXl4epmliGEaNQ0Cp29TG/k9t7P883cZ1+Wfojh07KC4uJjAwsMIvviLVcffddzvH2q9atcrjgYaInBlc/bnk9mC2jh07Ypoma9eurfa6ywcOHGDNmjUYhkHHjh3dLUVERERERHwgOTmZn376CYCuXbsqyBARr3M7zBg6dCjgmDH1wQcfJCcnx6Xz8vLyePDBB50zK1944YXuliIiIiIiIh62c+dO0tLSTrn/8OHDTJ48maKiIgCuueaa2ipNRM5gbs+ZMX78eN555x2OHz/Ohg0buOqqq3jooYe48MILK53F2DRNli5dyksvvcTevXsxDIPY2NhaWyNaRERERERct2zZMqZOnco555xD3759adWqFcHBwaSnp7Nhwwa++eYb55KYffv2rRPLdoqI/3M7zAgPD+fZZ59l8uTJ2O129u3bx+TJk2nUqBG9evWiRYsWhIWFkZ+fz6FDh9i0aZMz2TVNk8DAQP7xj38QFhbm9icjIiIiIiKeV1RUxM8//1xuOdeTnXvuubzxxhsEBATUYmUicqZyO8wAxxCRl19+mccff9w5YVZaWlqlE3qWnW80PDyc5557zjlURURERERE6paxY8cSEhLCihUr2Lt3LxkZGWRmZhIcHExcXBy9e/fmyiuv1O/0IlKrPBJmAAwfPpyePXsybdo0Fi9eTGFhIadaKCU4OJjhw4dz11130bp1a0+VICIiIiIiHhYTE8O1117Ltdde6+tSREScPBZmALRu3Zp//vOfPP7446xdu5Zt27aRlpZGXl4e4eHhxMTE0LVrV/r27UtUVJQnby0iIiIiIiIiZwiPhhkloqKiGDp0qLqaiYiIiIiIiIjHeSXMqI4xY8aQmJiIYRhs3brV1+WIiIiIiIiISB3n8zADOOXcGiIiIiIiIiIiJ7P4ugARERERERERkeqoEz0zpH6yWq1YLMrDqstqtWKaJoZh+LoU8RK1sf9TG/s/T7ex1Wr1yHVERETEQWGG1JhpmhoiVAMlr5leP/+lNvZ/amP/5+k21teJiIiIZynMkBozDEPvStaAYRjOd/v0+vkntbH/Uxv7P0+3sb5OREREPEthhtRYaGgo4eHhvi6jXir5BVmvn/9SG/s/tbH/82Qb2+12D1QkIiIiJTThgYiIiIiIiIjUKwozRERERERERKReUZghIiIiIiIiIvWKwgwRERERERERqVdcngD00KFDXimgqKjIK9cVEREREREREf/kcphx0UUXaVkxEREREREREfG5ai/NapqmRwtQQCIiIiIiUl7nzp2dzxMTEz16XtljygoKCiIiIoLIyEiaNm1Kt27d6N69OxdeeCHR0dEu3f/hhx9m9uzZLtcL8MMPP9CqVaty2y666CIOHjzo8jWq8xqJiH+oVpjh6SDDW9cUEREREZHqKSoqIiMjg4yMDJKTk1mzZg0AwcHBXHrppdxzzz20bt3ax1WKiDi4HGb88MMP3qxDRERERERq2b///W/nc9M0yc3NJSsrix07drBu3Tp27NhBYWEhCxYs4Mcff+Sxxx5j/PjxLl37uuuu45xzzqnyuNjY2FPui4mJ4dlnn3XpfiJyZnE5zGjZsqU36xARERERkVo2bNiw0+5fv349r776KqtWrSIvL4/HH3+csLAwrrzyyiqv3a1btyqvX5WwsDC3ryEi/qnac2aIiIiIiHjb/sz9pOalunx8XHgc8Q3jvVjRmal379589NFHTJkyhS+//BLTNHnkkUfo27cvzZs393V5InIGU5ghIiIiInXK/sz9dJ7WGWux1eVzQgNDSZycqEDDCwICAnjiiSfYunUrmzZtoqCggLfffpspU6b4ujQROYNZfF2AiIiIiEhZqXmp1QoyAKzF1mr15JDqCQoK4s4773R+PG/ePIqKinxYkYic6dQzQ0REREREqnThhRfSoEEDsrKyyMvLY9OmTfTt29er90xPT2fSpEkkJSWRlZVFREQEzZs3p1+/fowbN47u3bt79f4iUnepZ4aIiIiIiFTJMAx69erl/HjTpk1ev2deXh4rVqzg+PHjzqVjt23bxqeffsq4ceO49957ycnJ8XodIlL3qGeGiIiIiEgd1rlzZ1+X4FR2hcO0tLTTHvvII4/wyCOPnPaYOXPm0LVr10r3NW7cmPPOO4+uXbvSuHFjTNPk0KFDLF++nD/++AOARYsWsW/fPj799FPCw8Or+dmISH2mMENEREREXDZjywyeXPok2QXZXrtHoa2wRudd/unlBAcEe7iaUlEhUTx74bOM7zbea/eo6xo0aOB8npGR4bX7vPTSS/Tt2xeLpWJH8ttuu42ff/6Z+++/n8zMTLZs2cJLL73E008/7bV6RKTuUZghIiIiIi57+beX2Z663ddlVOpY3jHv3iDb8fnXdpjx73//2+Vj77rrLi9WAqZpOp8bhnHaY6+77jrOOeec0x7TqlWrSrf379//tOcNHjyYN954g0mTJgEwY8YM7rzzTpo0aXLa80TEfyjMEBERERGXPXTeQzzx0xNe75lRk2CicXhjr/fMePDcB712/VMZNmxYrd/zVLKyspzPo6OjT3tst27dvFr7oEGDOPfcc/ntt98oLi7m559/5qqrrvLa/USkblGYISIiIiIuG99tvNd7JqxNWUu/d/pV+7xvrv2Gvs29u7rGme7gwYPO5zExMT6sxGHgwIH89ttvAOzatcvH1YhIbdJqJiIiIiIiUiW73c6GDRucH5911lk+rMahbKCSne293kIiUvcozBARERERkSr9+OOPzmVQw8PD6d69u48rgvT0dOfzqKgoH1YiIrVNYYaIiIiIiJxWUVER//nPf5wfjxs3jsBA349YX7lypfN5u3btfFiJiNQ2r/0PlJOTw5EjR8jMzMRmszFgwABv3UpERERE/EhceByhgaFYi60unxMaGEpceJwXqzpz2Ww2nn32WTZv3gxAaGgot912m4+rglWrVvHrr78CEBAQwJAhQ3xckYjUJo+GGTk5OXzxxRfMnz+fHTt2OJduMgyDrVu3ljv2+PHjvP/++wAkJCQwZswYT5YiIiIiIvVUfMN4EicnkpqX6vI5ceFxxDeM92JVZ6aNGzfy8ssvs2rVKsDxe/2LL75I06ZNvXbPt956i2HDhpGQkHDKY1asWME999zj/Hj8+PFerUlE6h6PhRmrVq3igQce4NgxxzJaZdegrkxsbCy///4727Zto0GDBgwfPpzgYO8tpSUiIiIi9Ud8w3iFE7Xg+++/L/dxTk4O2dnZ7Nixg3Xr1pGUlOTcFx4ezpNPPskVV1zh1ZqWLFnCG2+8QUJCAgMHDqR9+/ZER0djmiaHDh1i+fLlznAFoHv37jz00ENerUlE6h6PhBmrV6/mlltuoaioCNM0MQyDDh06kJWV5Qw3KvOnP/2Jp556iqysLH777TcuuOACT5QjIiIiIiIuuOuuu6o8JiQkhEsuuYR77rmH1q1b10JVDklJSeXClMqMHDmSp556isjIyFqqSkTqCrfDjIKCAu677z4KCwsBGDt2LPfeey9NmjTh2Wef5bPPPjvluZdeeilTpkzBNE2FGSIiIiIiPhQYGEhERASRkZE0bdqUbt260aNHDy666CIaNmxYa3W8/PLLrF69mg0bNrBjxw7S0tLIyMjAZrPRoEEDWrduTb9+/Rg7diwdO3astbpEpG5xO8yYOXMmR48exTAMrrnmGp588kmXz23UqBFt2rRh7969FebUEBERERE5UyUmJnrtvJpe2xUvvvgiL774olvXSEhIICEhgT//+c8eqkpE/JHbS7P++OOPAERERHD//fdX+/yOHTtimib79u1ztxQREREREREROQO43TMjKSkJwzDo378/ERER1T6/pMtadna2u6WcEbZs2cJvv/3Gpk2b2Lx5MwcPHgTghx9+oFWrVj6uTkRERERERMT73A4zMjIyAGq8FJJhGADY7XZ3Szkj/Pvf/+aHH37wdRkiIiIiIiIiPuN2mBEeHk5WVhYFBQU1Or9ktZPo6Gh3Szkj9O7dm4SEBHr06EHPnj0ZN24cqamur8EuIiIiIiIiUt+5HWY0btyYzMxMdu7cWe1zTdNkw4YNGIahIRIuuu2223xdgoiIiIiIiIhPuT0BaL9+/QDYunUrycnJ1Tp3yZIlpKenA3D22We7W4qIiIiIiIiInAHc7plx+eWX8+WXX2KaJs899xxvv/22S+cdOXKE5557DnDMmzFixAh3S3Gy2Wzs2rWLzZs3s2XLFjZv3sz27duxWq0AjB07tkZLRv3www/MnTuXzZs3c+zYMSIjI2nTpg3Dhg3j6quvJjIy0mOfg4iIiIiIiIhUzu0wY9CgQQwYMIA//viDZcuW8be//Y0pU6bQqFGjU57z008/MWXKFFJTUzEMg8suu4yOHTu6W4rTPffcw7fffuux6+Xm5vLAAw84l6EtkZaWRlpaGuvWrePTTz/l9ddfp3fv3h67r4iIiIiIiIhU5HaYAfDyyy8zfvx4jh8/znfffceyZcsYNGgQhw8fdh7z/PPPk5qayrp168ptb9WqFVOmTPFEGU42m63cx9HR0URHR7N3794aXevvf/87P//8MwBxcXFMmDCBjh07kpmZyYIFC1i7di0pKSncdttt/O9//6NDhw6e+DREREREREREpBIeCTOaNWvG9OnTufvuu9m9ezcFBQUsW7YMKF169ZNPPnEeb5omAJ06deKtt96iQYMGnijDqVevXnTo0IHu3bvTvXt3WrduzaxZs3jkkUeqfa0ZM2Y4g4yOHTsyffp04uLinPv/8pe/8M9//pMPPviAzMxMnnzyST777LNKr/XQQw+xcePGat3/kksu4f7776923SIiIiIiIiL+yiNhBkCHDh34+uuv+eCDD/j88885fvz4KY9t0KAB119/PTfddBPh4eGeKsHpjjvu8Mh1bDYb06ZNc3780ksvlQsySjzwwAOsWLGCbdu2sXr1an755RfOP//8CselpKSwZ8+eatVQsnStiIiIiIiIiDh4LMwACAsL46677uL2229n8+bNrF+/niNHjpCTk0NYWBhxcXH06tWLvn37Ehwc7Mlbe8Uff/zhDBPOPvtsunfvXulxAQEBXHfddTz66KMALFy4sNIwo2zvFBERERERERGpGY+GGc6LBgbSu3fvej8Z5vLly53PhwwZctpjy+4ve56IiIhIXRYQEEBxcTE2mw3TNJ1DhEVERGqbaZrOOTADAgJOe6ylNgqqr5KSkpzPe/bsedpjGzduTPPmzQFITU0lLS3Nq7WJiIiIeEJJb1nTNMnLy/NxNSIicibLy8tzzrFZ1WgOhRmnUXZ+i1atWlV5fNljdu/e7ZWaRERERDyp7ETsaWlpzl8iRUREapNpmuU6BVS1UIhXhpn4i+zsbOfzRo0aVXl8dHR0ped60tKlS3nrrbecH2dmZgIwefJkZ3I1dOhQ7rrrLq/cvyyr1YrFojysuqxWq7rx+jm1sf9TG/s/T7ex1Wr1yHW8ITIyEsMwME2TnJwckpOTiYmJITw8XF/jIiLidSU9A9PS0sjJyQEcq6JGRkae9jy3w4yyq33UhMViITIykgYNGtC+fXu6dOlSZyYHLdvVMiQkpMrjyx6Tm5vrlZrS0tLYsGFDhe3btm1zPm/fvr1X7n0y0zT17k0NlLxmev38l9rY/6mN/Z+n27guf51YLBZatmzJwYMHnYFGTk4OhmFUOV5ZRETEXSVzNpUwDIOWLVtW+ca5R8IMT6b2QUFBXHLJJdx0002nXD3kTDZu3DjGjRvn6zIAxxeZ3rGpvpJ3v/T6+S+1sf9TG/s/T7dxXf86iYqKKhdogCOAKS4u9nFlIiJyJikJMqKioqo81iPDTE5+t6HkF4BTOd3+wsJCFi1axJIlS/jrX/9aK8MlTiU8PNw5jKOgoIDAwNO/XAUFBc7nERERXq2tLggNDSU8PNzXZdRLJb8g6/XzX2pj/6c29n+ebGO73e6BirwrKiqKhIQEcnJyyMrKorCw0DmjvIiIiLcEBAQQHBxMgwYNiIyMdHkqA7fDjMmTJwOQk5PD559/TlFREaZp0qJFC3r27EmzZs0IDw8nPz+fw4cPs3HjRg4dOgQ4hmX8+c9/Jjg4mMzMTBITE9m4cSM2m43i4mKmTZtGREQEkyZNcrfMGomKinKGGenp6VUGFBkZGeXOFREREalPLBYLDRo0qHLSNREREV/zSJixZ88ebr/9dgoLC+nZsyf/93//R//+/U95zurVq3nppZfYuHEjP/zwA++88w5t27YF4ODBg/zjH//gxx9/xDRN3njjDUaMGEFcXJy7pVZbu3btSE5OBiA5ObnKFU1KjoXam7dCRERERERE5Ezj9lIU+fn5TJ48mQMHDjB06FA+//zz0wYZAP379+ezzz7jggsuYP/+/dx9993OWb5btmzJW2+9xUUXXQQ4Zv+eOXOmu2XWSEJCgvP5pk2bTntsamoqKSkpAMTGxhITE+PV2kRERERERETOVG73zJg1axa7du0iNDSUF154gaCgIJfOCwoK4vnnn+fCCy9k586dzJo1iz//+c/O/Y8++ijLli3DbrezYsUK7rjjDndLrbbBgwfz/vvvA7B8+XJuvfXWUx67bNky5/OhQ4d6vba6QEuz1oyWdPR/amP/pzb2f2fS0qwiIiL1kdthxqJFizAMgwEDBlS7N0JMTAwDBw5k+fLlLFy4sFyY0apVK7p27crmzZvZs2ePu2XWyNlnn03jxo05duwYq1atYsuWLZWusGKz2fjkk0+cHw8fPrw2y/QZLUlYM1rS0f+pjf2f2tj/nUlLs4qIiNRHbocZ+/btA6B58+Y1Or9Zs2blrlNW+/bt2bx5s3MSztoWEBDAnXfeyZQpUwD4v//7P6ZPn05sbGy541555RW2bdsGQN++fRk8eHCt1+oLWpKwZrSko/9TG/s/tbH/O9OWZhUREalv3A4zsrKygPIreVRHyXkl1ymrZCm06g5lOHDgQIV5NhITE53Pt27dytSpU8vtP+eccxg0aFCFa02cOJHvv/+eX3/9lR07djB69GgmTJhAx44dycjIYOHChaxZswaABg0a8Mwzz1Sr1vpMS7PWnJZ09H9qY/+nNvZ/Z9rSrCIiIvWJ22FGbGwsKSkprFq1iqKiIpfnzAAoKipi1apVzuucLDs7G4BGjRpVq6ZDhw7x9ttvn3J/YmJiuXADIDAwsNIwIzAwkH/961888MAD/PTTTxw7doy33nqrwnHNmjVj6tSpdOrUqVq1ioiIiIiIiEj1uB1m9OnTh5SUFDIzM3n99dd58MEHXT73jTfeICMjA8Mw6N27d4X9JXNl+HplkMjISN5++22+//575s6dy6ZNmzh+/DgRERHEx8dzySWXcPXVVxMVFeXTOkVERMRNNivsn0Hw3q8xCtMwg2Og7VUQPwECQn1dnYiIiJzgdphx1VVXsWjRIgA++OAD8vLyuP/++4mMjDzlOTk5OUydOpXPP//cuW3ChAnljklPTycpKQnDMKrd22HgwIEVel54wrBhwxg2bJjHrysiIiJ1QPI8WDEJitIJwIKBHRMLpMyF1X+HQdOh1UhfVykiIiJ4IMw477zzGDlyJPPnz8cwDL744gvmzp3LBRdcQK9evWjevDmhoaFYrVYOHz7Mxo0bWbp0KXl5ec6xqMOHD+fcc88td9358+dTXFyMYRgMHDjQ3TJFRERETi15Hiwf4/zQwF7ukaIMWD4ahsyBVqNqvTwREREpz+0wA+D555/HarXy3XffYRgGeXl5LF68mMWLF1d6fNnlyS666CJefPHFCsdkZGQwduxYAPWGqKOsVmu1J2cVx+tWEuSJf1Ib+z+1sZ+xWQlbcQMABqdaQtXExIAVN5B/+a5qDzmxWq1uFikiIiJleSTMCAoK4s0332TGjBlMmzaNI0eOVLmeepMmTbj77rsrDC8p8be//c0TpYkXmaZZZTtLRSWvmV4//6U29n9qY/8SeHAWRlFGlccZmFCUgeXgbGytr67WPfR1IiIi4lkeCTNKTJgwgauuuoqff/6ZlStXsn37dtLS0sjLyyM8PJxGjRrRpUsXBg4cyODBgwkICPDk7aWWGYahdyVrwDAM5zu6ev38k9rY/6mN6zHTjpGfjJGThCVnJ0b2DgIPfY0JuNKSJhYCU+Zjj7+mWrfV14mIiIhneTTMALBYLAwdOpShQ4d6+tJSx4SGhhIeHu7rMuqlkj+C9Pr5L7Wx/1Mb13GF6ZCVCFlJkJ3oeJ6dBNk7HCuW1JCBnUBbJoHVbHe73V7je4qIiEhFHg8zRERERGqFrQBydlUMLLISoSC1WpcyTXCl84TNboHAGNS3VERExLcUZoiIiEjdZZqQf7B8UFESXuTuBbMaPR4sQRDZARp0hqgE5+PKbzcy0DLZpUsEWOz8mjyW82r22YiIiIiHKMwQERER3yvKOmlYSJlHW171rhXWokJgQYPOENEWLOV/9cnIgCc+GsAXE58gOjwDi+XUE3Xa7QYZedG8+d14zru2+p+iiIiIeI7XwowjR46Qnp5OTk6OyzN4DxgwwFvliIiIiK/ZiyBnd+XDQqxHqnetwMiTAovO0CABojpBUFS5Q00TDh+GbX/Atm3l/6WkAIRyQ9p05t43GrvdqDTQsNsdY1BueHs6OQ2rtyyriIiIeJ5Hw4y1a9fy6aefsmLFCjIyMqp1rmEYbN261ZPliIiISG0zTbAernxYSM5uMG2uX8sIgMj2pUFF2fAitFmFSS7sdti7t2JgsW2bowfG6SxYN5IxU+fw0e2TiIlMx2a3EGCxOx8z8qK54e3pLNowkjFjqvuiiIiIiKd5JMyw2+0899xz/O9//wO0lvqZwmq1YrFYfF1GvWO1Wp2rIIh/Uhv7P7UxUJyDkbPDsbxpzg4sOTtKPy7OrtalzJAm2CMTMCM7Yo/shBnZyfEY0RYswRWOLyyAnVusJCZaSEw02L7dwvbtFnbsMLBaXW+TuDiTzp3tBASYLF8eyPy1o2gx+RDjz57J2P6ziYlMIy0nhtmrxzJz1XgKihw9MoYPLyAvrxqhDI6vGREREfEcj4QZ//znP/n888+dH3fo0IHs7GyOHj2KYRj079+f3NxcUlJSSE9PBxw9McLCwujevbsnShAfME1TwVUNlLxmev38l9rY/50xbWwvxsjbhyW3NLBwhhbWlGpdygwIxx7ZETPCEViUhhYdICi60nNyciApySgXWiQmWti928Bmcz20aNXKTpcudjp3doQXJf/i4hz7rVbo2DGAzEwoKArls1+v5bNfK06KYRgmDRvCmDHFVLfZ/frrRERExAfcDjN27drFxx9/jGEYxMTE8J///IdevXrx7LPP8tlnnwHwySeflDv+888/54svviA/P5927drxxBNPEBQU5G4pUssMwziz35WsIcMwnO/o6vXzT2pj/+dXbWyaUHjM0aMieweWnCSMnJ2O0CJ3N4ZZ5PqlsGCGtyntYRGV4AgwIhMwQ5uDUbE3nwGkpkJiouVED4vS8CI52fXefwEBJh06lIQVjscuXUwSEuxERlZ2Rmm7hYXBu+8WMnFiMIZhYpoV29QwHGHEu+8WEhZW/Tav918nIiIidYzbYcZXX33l/IXuH//4B7169Trt8R06dOCJJ55g+PDh3H777cyYMQOLxcLTTz/tbilSy0JDQwkPD/d1GfVSyfeMXj//pTb2f/WujYvzIHtHxXksspKgKKN61wqJK527osw8FkZkB4yAEAACTjrFNOHAgcrns0hNdf3WoaHQpQt07Vr+X6dOBsHBBlCz4Y/jx8OcOTBpEqSng8ViOicDtdsNoqMNpk+HkSNDanR9u70aS8iKiIhIldwOM1avXg1A06ZNueCCC1w+r1+/fjzzzDPcd999fPnll4wYMYL+/fu7W46IiMiZy26DvP2VL2+at7961woIdawMcvLyplEJEBJzytOKi2HXroqBxfbtjmEjroqOrhhYdO0KbdpAwMlJiYeMGgWHDsHMmTBzpo20NIOYGJPx4wMZP94RpIiIiEjd4HaYcejQIQzDoGfPnuW2l+1OWVRUVOkwkuHDh/Paa69x8OBBZs+erTBDRETEFQXHKwksEiF7J9gLqnEhAyLiKw8sIuIrHRZSIj8fEhMrhhZJSVDk+sgUmjcvH1Z06+Z4bNq0wmIltSI0FK69FsaNKyzT+8ZrK9mLiIhIDbn90zk72zFjeUxM+XdpyoYXeXl5NGzYsNLze/fuTXJyMmvXrnW3FBEREf9hszrCiZJhIWUfC45X71pB0RWGhNCgM0R2hMCw056akVExsNi61bEEqqtzWhoGtGtXMbDo0sXRA0NERESkutwOM4KDg8nPz68wFjQqKsr5PCUl5ZRhRknocfToUXdLERERqV9MO+QlVz6PRe5eoBorYFiCHOHEyYFFVIJjjovTdHMwTTh8uHxYUfL88GHXSwgKgoSE8oFF166ObWGnz0xEREREqsXtMKNJkybs27ePrKysctvj4+Odzzdt2kSXLl0qPX/v3r0A2GzVW69dRESk3ijMOMWwkB1gy6/etcJaVuxlEZUAEW3Acvof6zYb7NtXMbDYtg0yM10vITLS0auibGDRtSu0bw+BGpEhIiIitcDtXzk6derE3r172bdvX7ntPXr0cD6fNWsWEyZMqHDuxo0bWb9+PYZh0Lx5c3dLERER8R1bIeTsriSwSAJrNXsfBkZVHlhEdYKgStcZLaegAHbsqDg8JDERrFbXy4iLqxhYdO0KrVr5Zj4LERERkRJuhxn9+vXju+++Y+fOneTm5hIREQFA27Zt6datG1u3bmX9+vU88cQT3Hvvvc65NVavXs3DDz/snFzrvPPOc7cUERER7zJNyD+E5dhGjOwkLLk7IX/3iWEhe8CsRi9DIxAi21cMLBp0hlDXZr/MznasEnJyaLFrl6MXhqvi4ytfOSQuzvVriIiIiNQmwzRdnb6rcrt27eLKK6/EMAxef/11LrvsMue+pUuXcscddzhXNrFYLMTExFBQUOCcONQ0TcLCwliwYAEtW7Z0pxTxspycHBITE50ft2nThvDwcB9WVD9ZrVZniBeqdf78ktrYDxRlY+TuxJKdhCVnJ0bOicfcnRjF1VhfFDBDmmKPTMCM7Ig9shP2KMdzM7ytY54LFxw7BomJFhITLWzfbjgfDx489WojJwsIMOnQwaRzZzudOzseu3QxSUiwE1l1Z48zjqe/j/Py8sr1Yu3cuTOReuFFRERqzO2eGR06dOCyyy7j8OHDbN26tVyYccEFF3DXXXfx73//G3DMi5GamkrZ/CQ0NJRXXnlFQUY9ZJombmZhZ6SS10yvn/9SG9cT9iKMvH1YcnZg5OzEkpNU+rygGrNeAmZABPbIjqWBRWQCZkRH7JEdIajBaU40yz1NTjZOhBYG27db2L7dEWCkpbk+piM01BFQlAQWJf86djQJDq6yDDnB09/H+r9ARETEszwyTdcbb7xxyn133303ffv25f333+ePP/6g6MTi81FRUQwZMoQ777yTDh06eKIMqWWGYTh73YjrDMNwvtun188/qY3rENOEgiMnelfsOBFW7DjRy2I3hlns+qWwsOdYO7Yld2bHkU5sP9SFnUc6se1gF/LM5rz7bhHDLyw/tqOy1i8uht27S3tXOAILg6QkC7m5rn+9REef3MvC0dMiPt7EUmmHDX0tVoenv4/1f4GIiIhn1cqc4+eddx7nnXcedrud9PR0DMOgUaNG+sFez4WGhmqYSQ2V/IKs189/qY1rWXGuY2WQk5c3zU6Eoqyqzy8rpHGFeSx+XNOZKyZ0oMgWXGkvBsOAiRNDmDMHRo1ybMvPd0y4efKqITt2wIlc3yXNm5fOYVF2Ms6mTQ0MI6B6n5tUiye/j09ewl5ERETcU6sLqFksFmJjY2vzliIi4i/sNsjbd1JgcWK1kLzk6l0rIPTE6iAnTbzZIAGCG5U71GqF8TdBke3UwzFKtk+cCBdcAElJsHev68M3DAPatasYWHTpAtHR1fvURERERM4EbocZY8eOBSAkJIRPPvmEoCDXJjMTERGpwDSh4Hjly5tm7wR7YTUuZkBEmzJBRZnQIrwVGK5NnjljBqSnu3bHggJYsuTU+4OCICGh4nKnCQkQFubaPURERETEA2HG9u3bARg6dKiCDBERcU1xPuTsLA0qyva2KHQxOSgR3AiiOlcMLCI7QGDNEoKcHNiwAdatg1deqf75kZGVL3Xavj0E1mqfSBERERH/5PavVNHR0WRkZNCkSRNP1CMiIv7CtEPegcrnscjdD1RjdQdLMER1qjgsJCoBQuPcKvP4cUdosW4drF3reExKqvkKH+ecA7/95hg6IiIiIiLe4XaY0axZMzIyMsjOzvZEPSIiUt8Uplc+j0X2DrBZq3et8NaVz2MR3gYs7k12aZpw6FBpYFHyuH+/W5ctx2KBFi0UZIiIiIh4m9thxpAhQ9i2bRtr1671RD0iIlIX2QogZ1f5YSEljwWp1btWUIPSYSFlA4uoThAY4ZFy7XbYvbticHHsWNXnBgdDz57Qpw/07QuHD8Mzz7h+3xNTSYmIiIiIF7kdZowfP56PPvqIo0ePMnPmTMaPH++JukREpLaZJuQfrHwei9y9jmEjrjICIapDxdAiKgFCm3i060JRkWPJ07JDRdavB1c6DEZGQu/epcFFnz6OyTnLTgFltcKbb0JGxumHnhiGY+UR/RgUERER8T63w4zWrVvz6KOP8tRTT/HMM88QFhbGlVde6YnaRETEGwozT4QVJw0LyUoCW171rhXWvPLAIrIdWDw/02V+PmzaVL7HxaZNjlVEqhIXVz606NMHOnZ0DA05ndBQmD4dRo92BBaVBRol2cz06Y7jRURERMS7DNOs6RRnDocOHQJg8eLFTJ06FZvNRq9evRg+fDjdu3cnJiaGUBd/s2vRooU7pYiX5eTkkJiY6Py4c+fOREZG+rCi+ikvLw/TNDEMg/DwcF+XI15QJ9rYXgQ5uysJLBLBeqR61wqMrHwei6gECIryTv04ekKsX1++x8X27WCzVX1u69YVg4tWrdzrEDJvHkya5Fim1WIxsdsN52OjRo4gY+TIml9f6hZPfx/rZ6iIiIhnuR1mdOnSBaPMb4clP/irXYhhsHXrVndKES87+RexNm3a6I/xGrBarc7vE1eDPqknbFYCDs7CODgXozANMzgGs+VobC3HQYAX2to0oeAwluwdGLk7sWQnYcnZiZGzAyNvD4bpwl/9JZcyAjDD22JGdsIe2REzMsH5aIY28/qMlkeOwPr1FjZutLBhg4X16y3s2VNFlwnAMEw6djQ56yx7uX9x7i1wckpWK8yeHcCcOQbp6QaNGpmMGWMydqxNPTL8jKf/r87Ly2Pfvn3OjxVmiIiIuMdjfYBLfuCXBBluZiRSD5imqXaugZLXTK+ffwlIWUjIutsxijIwsWBgdzweWYC56UEK+r6Drdnwml28OMcZUlhydmDJ3YmRfeKxuHorSZkhjbFHdsIe0elEcHEivIho51j+9JQneuZr1TRh/36D9esdocXGjY7g4vDhqoOLwECTrl3LhxY9e9qJqqRziLe+tUJC4OqrixkzpnSVlpI/dPXt7F88/X+1/r8XERHxLLfDDA0NOXOVDa/EdYZhVAj/pH4LSFlI8KqrnR8b2Ms9UpRJyMo/UTjwS2zNTzGnkL0YI2+fM7Ao92hNqVY9ZkAYZkRH7FEJJx5PBBcRHSE4utJzvPGVaLPBjh2lwUVJeJGeXvXdwsJMevZ0BBa9ezseu3UzCQmpjcqrpu9j/+fpNtbXiYiIiGe5HWb8+OOPnqhD6qHQ0FANM6khn8+nIJ5js8La2098UPk7rwYmYBCy9nYYvh5y91Vc3jRnl2OeC5cZENG2knksOmOEt8QwLFTd18FzCgpgy5bSiTnXrYMNGyDPhflEo6Mrzm/RubNBQEAAEODt0mtM38f+z5NtbLdXYzUgERERqZLnp5oXETmT7J8BRekuHGg6jpvbpnrXD4k9sVpIQvnHqA7emYfDBTk5jqCibHCxZYtjidSqNG9eMbho29brU3KIiIiIiJ9RmCEi4o7kOYAFcONdV0sIRHWqGFg0SHCEGT50/HjpSiIlwUVSkmvzQ7RvXzG4aNbM+zWLiIiIiP9TmCEiUlM5eyF9I9UKMoKjoc1fyg8PCW8NFt8OpzBNOHiwfHCxdi0cOFD1uRYLdO1aPrjo3dsxfERERERExBu8FmYkJSWRkpJCVlYWNpuNMWPGeOtWIiK1wzQhY6OjN0byHEhfX80LWKDpRTBgmudrqwa7HXbtqhhcpKZWfW5ICPTsWT646NkTNG2EiIiIiNQmj4YZBw8e5L333mPhwoVkZ5dfLvDkMCM1NZXnnnsO0zTp0aMHt956qydLERHxDHsxHPu1NMDI3evOxaDVWM/U5aKiIti2rXxwsX49ZLuwomtUlKOHRdlhIl27QlCQt6sWERERETk9j4UZCxYs4MknnyQ/P7/CWuqVLUcWFxfH8ePH+eOPP1i+fDl//vOfiYiI8FQ5IiI1V5wPh7+D5NlwcD4UHK/8uJj+0OJK2D4VirM51WomDoZjiEn8eC8U7JCfDxs3lg8uNm1yrDRSlcaNK85v0aGDYwiJiIiIiEhd45EwY8mSJTz44IOAYxmzBg0a0Lt3b/bv38/evXtPed6ECRP4448/sFqt/Pzzz1x++eWeKEdEpPoKjsPBhY7eFylLwFbJmqJGIDS9AFqNgZajIKK1Y3tMP1g+GjCoPNA4EeieM91jK5BkZDh6WJQNLrZtcwwhqUp8fMXgomVLrSgiIiIiIvWH22FGVlYWTzzxBKZpYrFYuOuuu7jtttsIDg7m2WefPW2YcdFFFxEYGIjNZmPFihUKM0SkduXug+S5jgDj6HIwbRWPCYyA5lecCDCGQ3Cjise0GglD5mCumIRRlI7NbiHAYnc+mkHRGIOmO46rgcOHS1cSKQkudu+u+jzDgISEisFFrG8XSBERERERcZvbYcaXX35JVlYWhmFw1113cdddd7l8bmRkJO3btycpKYnExER3SxEROT3ThIxNZSbwXFf5cSGNodVoR4DR7GKXelPMWzuK2+86xMUJMxk3YDaNItJIz41h1h9j+SFpPO+8H8rIVlWXt3dvxeAiJaXqTy0oCLp3Lx9cnHUWREZWfa6IiIiISH3jdpixfPlyAKKjo2s0iWe7du1ISkrigCvr/4mIVJfdBqm/woE5Jybw3FP5cZEdoPVYR4ARe061lkqdNw8ccxyH8tmxa/ns12vL7TcMGD0a5syBUaMc22w2SEysGFxkZFR9v/BwR1BRNrjo3t2x0oiIiIiIyJnA7TBjz549GIZB//79CQ4Orvb5DRs2BKiw+omISI05J/Ccc2ICz1OsORrT3xFetBoDDbvVaNIIqxUmTXI8N08x/6dpOi59zTXwl784JuXcuBHyKpmW42SNGlUcJpKQAAGuZy0iIiIiIn7H7TAj48TbiDExMTU632ZzjFG3aMr8esdqtardasBqtWKaZqWr/IgbCtMIOLyYgJQFBBz9HqOSCTxNIxB73GBszUdia3YlZniZcR/5+TW67eefB5CeXnWXCNN0hBfvvnvqY5o3t3PWWSa9e9s56yzHv/h4s0LG4srqJOJd+j72f55uY6vV6pHriIiIiIPbYUZUVBQZGRnkufIWYyWOHDkCOIapSP1immaFZXilaiWvmV4/9xl5Bwg4vIDAlAVYjv+CUckEnmZABLYmwyhuPhJb08vKT+Dpgdd//vwALBYTu716f/C0b2+nVy+7M7jo1ctG06aVH6svk7pH38f+z9NtrK8TERERz3I7zGjatCnp6els37692ucWFRWxfv16DMOgbdu27pYitcwwDL0rWQOGYTjf7dPrV02miZG1hYCUBQSmzMeSub7yw4LjsDW/kuLmI7E3vgACwgDnAqkedeCAUa0go1cvG998U8CJEXZl6GuhPtH3sf/zdBvr60RERMSz3A4zBg4cyPbt29m5cyfbt2+nS5cuLp87a9YscnJyMAyDc845x91SpJaFhoYSHh7u6zLqpZJfkPX6ucBug9TfSlcgyTnFmqSR7aGVYwJPI24QgZYA9/+DO4XcXPjyS/jvfx2TdrrKYoGOHQNo3lzt7g/0fez/PNnGdrvdAxWJiIhICbd/1x8xYgTTp08H4Omnn+bjjz92aSLQpKQkXn75ZQACAgIYVTLFv4hIcT4c/v7EBJ7zTjOBZ78yE3h2r9EEntWxebMjwPjkE8jMrP75djuMHev5ukREREREzjRuhxk9e/bk0ksv5dtvv2XDhg3ccMMNTJkyhYSEhEqPt1qtzJw5k9dff93ZK2PChAm0aNHC3VJEpD4rSINDCx0BxqFvoJIJPDECoMkFJwKMURAR7/WyrFaYORPefht+/bXi/l69YOdOx/yhpxsSbxgQHQ3jx3utVBERERGRM4ZHemE/99xz7Ny5k927d7N+/XpGjx5Nx44dy83cfdddd5Gamsq2bdsoKipyToTVtWtXHnnkEU+UISL1Te5+SJ7rCDCOLoNKJvAkIBxaXOEIMFoMh5CarZxUXYmJ8M478NFHkJZWfl9YGFx9NdxxBwwYAAsWwOjRjsCiskCjpMPI9OkQGur10kVERERE/J5HwowGDRrw8ccfc99997Fq1SoAdu7cCZROePXjjz8C5WfzPuecc3j99dddGpYiIn7ANCFziyO8ODAb0tdWflxIY0fPi1ZjoOnFEBhWK+UVFsLs2Y6hJD/9VHF/9+5w++1w3XWOXhYlRo6EOXNg0iRIT8e5uknJY3S0I8gYObJWPg0REREREb/nsfnx4uLimD59OnPnzmX69Ols27btlMd26NCBW2+9lVGjRmGxWDxVgojURXYbpK4oM4HnrsqPKzOBJ3GDwBJQayXu3u3ohfHhh3D0aPl9ISEwYYIjxDjvvFNPyzFqFBw65BiSMnOmjbQ0g5gYk/HjAxk/Xj0yREREREQ8yTC9tPD5sWPHWL9+PUePHiU7O5uwsDDi4uLo1asXrVu39sYtxctycnJITEx0fty5c2ciIyN9WFH9lJeX5/+rIBTnw5EfTgQY86DgWOXHNerrCC9aj4GGPbw+gWdZRUUwf76jF8a331bcn5DgCDBuuAFiY6t37TOijc9wamP/5+k21s9QERERz/LWyoU0btyYSy65xFuXF5G6pjAdDp6YwDPlGyjOrXiMEQBNhp6YwHN0rUzgebL9++Hdd+H99yElpfy+oCAYN84RYlxwQa1mKyIiIiIiUg1eCzNE5AyQe6DMBJ5LTzOB5+UnJvC8stYm8CzLZoPFix0rkixe7Fgitax27RwBxo03QpMmtV6eiIiIiIhUk9thxgcffMCIESNoor8ARPxf2Qk8k+dA2prKjwuJg5YnJvBsNqzWJvA82aFDjh4Y774LBw6U3xcQ4Jjn4o47YNgw0PQ9IiIiIiL1h9thxksvvcSrr77KwIEDGTVqFJdeeqnGD4v4E1cn8IxoB61LJvA8t1Yn8CzLbofvvnPMhTFvnqNXRlmtW8Ott8LNN0OLFj4pUURERERE3OSRYSZ2u50VK1awYsUKpkyZwkUXXcSoUaMYPHiwVisRqY9sVjh8YgLPg/PAerTy43w4gefJjhxxrEby7ruO1UnKslhg+HDHUJIrrnD0yhARKbE/cz+peanltuVb88EEDAgLLd+7LC48jviGtT/nj4iIiJRyO8w499xzWblyJbYTb3/m5+ezaNEiFi1aRExMDFdeeSWjRo2iR48ebhcrIl5UmA4HF52YwHPxaSbwHFJmAs82tV1lOaYJS5c65sKYPduxQklZzZvDLbc4/sXr7w4RqcT+zP10ntYZa7HV5XNCA0NJnJyoQENERMSHPDJnxrFjx1i4cCHz5s1j69atlKz2evz4cT755BM++eQT2rVrx+jRoxkxYgQtW7Z0u3AR8YC85NIJPI8sBbO44jEBYdD8xASeLa+EkGquU+oFx4/D9OmOoSRJSRX3X3qpYy6MESMcK5SIiJxKal5qtYIMAGuxldS8VIUZIiIiPuSRYSaNGzdm0qRJTJo0id27dzN37lwWLlxIcnKyM9jYs2cPr7/+Om+88QZ9+vRhzJgxXH755URFRXmiBBFxhWlC5tYyE3iurvy4kNiTJvD0/Tw4pgm//urohTFzJhQUlN/fuLFjHoxbb4X27X1To4iIiIiI1A6PL83avn177r33Xu69917Wrl3L/PnzWbx4MRkZGQCYpsnatWtZu3Ytzz77LBdccAGjRo1i6NChBOktVBHPs9vg+O+O8OLAHMjZWflxEW2h1VjH/Bdx54KlbqzcnJEBn3ziCDG2bq24/8ILHb0wxoyB4ODark5ERERERHzBq3+t9O3bl759+/LYY4/x888/M2/ePH766SesVkd3zsLCQr777ju+++47GjRowMqVK71ZjsiZw+UJPPucmP9iDET39OkEnmWZJqxa5RhG8sUXkJ9ffn9MDEyaBLfdBp07+6REERERERHxoVp56zUwMJALL7yQCy+8kNzcXJYsWcKCBQv4/fffMU0T0zTJysqqjVLEg6xWq1arqQGr1YppmhieDg4KMwg48g0BKQsIOPIthq3iBJ6mEYA99jxszUdiaz4CM7zMeO+TEwMfyM6GL78M5P33A9m4seLX1rnn2rjppmLGjrURGurYlpdXy0W6wGttLHWG2rh+yy/KZ2f6Tnam72T5/uU1u4Y1n7xq/AdU8kaOiIiIeEat9yOPiIhg3LhxtGvXjvDwcL7//vvaLkE8pCSIkuopec088foZ+QcJOLyQwJQFWFKXY1QygacZEIat8cXYmo+iuNllEFxmAs860n4bNlh4771AZswIJCen/B+HDRuaXHNNMTfdVES3bqX11pHSK+XJNpa6SW1c99lNO4eyD7EjfQdJaUnsSNvBjvQd7EjbwYGsA5i42W4m1Wp7fZ2IiIh4Vq2GGXv27GH+/PnMnz+f5ORkAAzD0A/4esowDL0rWQMlX/M1ev1MEyN7u6P3Rcp8AjLWVH5YcCy2Zlc4emA0vsg5gWddaq3cXJg5M4D33w9kzZqACvv797dx883FjB9vI9w5/2hd+gxOza02lnpBbVx3ZBVksTN9Z2lgkeYIL3Zl7CKvyItdtwyq1fb6OhEREfEsr4cZx48fZ8GCBcyfP58tW7Y4t5cNMDp16sTo0aO9XYp4WGhoKOHhvl/lot6wWWH/DIL3fo1RmIYZHENg26sgfgIEhJ76PNMOqb+XrkCSvaPy4yLaOue/MBqfR6AlsPa7Xrlg82bHXBgffwwnjy6LjIS//AVuvx369AkAKoYc9UXJH7r6HvFfauPaU2wvZm/GXhJTE0k8nlj6eDyRwzmHq3WtBiEN6BzbmYTYBDrHdiYoIIhHfnik2jWFhYZVq+3tdnu17yEiIiKn5pW/dfLy8vjuu++YN28eK1euxGazAeUDjKZNm3LllVcyatQounTp4o0yROqO5HmwYhIUpROABQM7JhZImQur/w6DpkOrkaXH26xw+McyE3geqfy6jXqXmcCzV52ZwPNk+fmO5VT/+1/H8qon69PHsSLJNdeAVmsWOXOl5qVWCCySjiexM20nRfYil68TYATQvlF7Z2DROa6z87FpRNNyvSTWpqytUZghIiIivuWxMMNutztXLPnxxx+dE12VDTAiIiK49NJLGTVqFOecc466XMqZIXkeLB/j/NDAXu6RogxYPhrO/fTE8XPg0GIozql4LcMCjYecCDBGQ2RbLxbuvsRER4AxfTqkpZXfFxbmCC9uvx0GDKizOYyIeFhBcQE703Y6A4uktCRncJGWn1b1BcqIC49zhBQnBRbtG7UnOEBrNYuIiPgzt8OMDRs2MG/ePBYvXkx6ejpQPsAIDAzk/PPPZ9SoUVx88cWEhIS4e0uR+sNmdfTIADjlZHMntv/2l8p3B4RB88scAUaLKyE0zrM1elhBAcye7Qgxli6tuL9HD0eAce21EB1d29WJSG0wTZND2YecPSvKDgvZm7EXu+n6kIvggGA6xXQqDStOBBYJsQnEhMV48bMQERGRusztMONPf/pTpZN49urVi1GjRjF8+HBiYvTLhpyh9s+AovTqnxcSCy1HOgKMZpc4J/Csy3btgnfegQ8/hGPHyu8LCYGJEx0hxrnnqheGiL/IKcwh6XhShcAi6XgSOYWV9C47jZZRLSsEFp1jOxPfMJ4Ai/fmz4kLjyM0MBRrsetLp4YGhhIXXreDZREREX/nkWEmJUFG69atGTVqFKNGjaJNmzaeuLRI/ZY8B7AALr4LGdkRBr4Hjc8DS12cvrO8oiKYPx/efhu++67i/oQEx1wY118PsbEV94tI3Wez29ifub/CxJuJqYkczD5YrWtFBEVU2sMiITaByOBIL30GpxffMJ7EyYmk5qWW255vzXd0nDMck32WFRceR3zD+FqsUkRERE7m9l9L0dHRXHHFFYwaNYo+ffrU6Br5+fksWbKEMWPGuFuOSN1ScByXgwyA8FbQdKjXyvGU/fvh3Xfh/fchJaX8vqAgGDfOEWIMHapeGCL1RXp+eqWBxc60nRTYCly+jsWw0Da6rTOwSIhNcAYYLaJa1Mn5suIbxlcIJ/Ly8rRijYiISB3mdpjxyy+/EBhYs8usXLmSOXPmsGTJEvLz8xVmiP8JrM47jRYIqbtDsmw2WLTIMRfG4sVw8iqD7dvDbbfBjTdCkya+qVFETq/QVsju9N2VrhhyLO9Y1RcoIyYsprR3RUxpYNExpiMhgZofS0RERLzL7TCjukHGvn37mDNnDnPnziXlxFu6Je98iPgN0w67P4QjS6txkh1ajfVWRTV28KCjB8Z778GBA+X3BQTA6NGOuTCGDQOLxTc1ikgp0zQ5knukXGBRsmLI7vTd2Eyby9cKsgTRMaZjhcCic1xnzRkhIiIiPlUrg/JzcnJYtGgRs2fPZv369QAVJgwNDtYSauInMrfCqjvg2M/VOMmA4GiIH++tqqrFbnfMgfH22445MWwn/e3TurWjF8ZNN0GLFr6pUaS27M/cXyfnU8grymPH8R2VLnGaVZBVrWs1j2zuGA5y0hKnbaPbElgP5u8RERGRM4/XfkMxTZOff/6ZOXPm8OOPP1JQUODcXsIwDPr378+oUaO47LLLvFWKSO0ozoct/4BtL4G9qHR74yFlgo3Klmc90SvpnOkQEOrtKk/ryBHHaiTvvAN79pTfZ7HAlVc6emFcfrmjV4aIv9ufuZ/O0zpXe6WLxMmJHgk07KadA5kHKl3idH/m/mpdKywwrNz8FWUn4GwQ0sDtWkVERERqk8fDjB07djB79mzmz59PaqrjnayTe2F06tSJUaNGMWLECJo3b+7pEkRqX8q38MedkLOrdFtkBxjwFjS/FJLnwe+ToDAdEwsGducjwdGOIKPVSJ+Ubprw00+OuTBmz3asUFJW8+Zwyy2Of/GavF/OMKl5qdUKMgCsxVZS81KrFWZkWjMdYcVJE3DuOL6D/OJ8l69jYBDfML7SJU5bNmiJxdBYMBEREfEPHgkz0tPTWbBgAbNnz2bbtm1AxQCjZE6Mnj178tVXX3nitiK+l38Y1t4H+/5Xus0SBF0fgu6PQeCJ7uetRsHYQ7B/Jra9MzEK0zCDYwhsO94xtMQHPTKOH4ePPnL0wkhKKr/PMODSSx29MEaMcKxQIiLuKbYXsyd9T6UrhhzJPVKtazUMaVhpYNExpiNhQWFVX0BERESknqtxmFFcXMzSpUuZPXs2y5cvp7i4GCgfYoSEhHDxxRczevRobr/9dgzDwKIZAsUfmHbY+Q6sfxiKMku3Nx4MZ78NDbtVPCcgFNpdS2HTcc5JbwNrebk/04RffnH0wpg5EwpOWm2xSRPHPBi33upYnUREamZdyjrWpawrF1jsSt9Fsb3Y5WsEWgJp36h9hSEhnWM70ySiiSbOFhERkTNatcOMzZs3M2fOHBYsWEBmpuOPuMrmwRgzZgyXXXYZkZHVWZpSpB7I2ASrbofUFaXbgmOgz8vQfhLUwW7cGRnw8ceOEGPr1or7L7rI0QtjzBjQXLwi7rtl/i0uH9skokmlgUX7Ru0JClC3KBEREZHKuBxmvPfee8yZM4dduxxzApw8jKR9+/aMHj2akSNH0kLLG4g/Ks6FTc/A9leh7NKG7a6HPq9AaGPf1VYJ04RVqxwBxhdfQP5Jw+5jYuDGGx2rkiQk+KZGkTNFaGAonWI6OYeDlF05JDo02tfliYiIiNQ7LocZr7zyCoZhlAsxYmJiGD58OKNHj6Znz55eKVCkTji4CFbfCbn7SrdFJTiGlDS90Hd1VSI7Gz77zLGs6oYNFfeffz7ccQdcdRWE+nbxFJE6x1psZdORTaw+tJo1KWtYvm95ja4zoesEzm9zvjOwiG8Yr8k3RURERDyoRnNmhIWF8dBDD/GnP/1Jc2CIf8s7BGv+Dgdmlm6zBEP3R6HbwxAQ4rvaTrJ2raMXxmefQW5u+X0NG8L11zuGknTv7pv6ROqaguICNh09EVwcWsPqlNVsPrq5WvNanMrDgx+mb/O+HqhSRERERCpT7TDDMAysVivPPvssS5YsYfTo0Vx66aVERER4oz4R37DbYMdbsOExKM4u3d70QhjwH2jQ2Xe1lZGb6xhC8t//wh9/VNw/cKCjF8bEiVDLc42K1CmFtkI2HdnEmpQ1zl4Xm45soshedNrzDAxMzNMeIyIiIiK1z+UwY8SIEfzwww/knxh4b5omK1euZOXKlUyZMoWLL76YUaNGMXjwYPXWkPotbR2sug3SVpduC4mDvq9B22sd65b62KZNjgDjk08gK6v8vshIuPZaRy+M3r19Up6ITxXaCtl8dLOjt8WJ4GLjkY1VBhcWw0LXuK70a9GP/s37069FP0zT5PwPz6+lykVERETEVdWaMyM3N5fFixczd+5cVq9e7Zw/w2q1smjRIhYtWkRMTAwjRoxg1KhRdFd/dqlPinJg45OQ9IZj6dUSHW6G3v+EkFjf1YZjAs+ZMx1zYfz2W8X9ffo4emFccw1ERdV+fSK+UGQrcgQXKWucQ0U2HtlIoa3wtOcZGHRt3JV+zfvRr3k/+rfoT+9mvYkILt/LcG3KWm+WLyIiIiI1VK1hJhEREYwfP57x48eTnJzMnDlzmDt3LgcOHHAGG8ePH+fjjz/m448/LrfCiUidljwXVk+GvOTSbQ27wYC3oclg39UFbN8O77wDH30E6enl94WHO8KL22+H/v3rRKcREa8pshWx9dhWZ2+L1YccwUWBreC05xkYdI7rTP8W/csFF5HBWjpcREREpL6q0QSgAK1atWLy5MlMnjyZ1atXM3v2bJYsWUJOTo4z2Ni9ezdTp07l9ddf91S9Ip6VewDW3O0IM0oEhEKPJ6DLAxAQ7LFbWa0wYwZ8/XUwaWkGMTEmV10FEyZUXFWkoABmz3YMJVm6tOK1evRw9MK49lrH5J4i/qbYXlwaXJzocbHh8AaXgouE2IQKwUVUSM26K8WFxxEaGIq12OryOaGBocSFx9XofiIiIiLiGsMsu9aqmwoKCvj222+ZM2cOK1aswG4v7apfsqxrYGAgQ4YMYdSoUVx00UUEB3vuj0XxrpycHBITE50fd+7cmcjIevrOpr0YEv8Fm56E4jJLfzS7FAa8BVEdPHq7efNg0iRHzwqLxcRuN5yPjRrB9OkwciTs2uXohfHBB5CaWv4aISGOiTzvuAMGDVIvjLosLy8P0zQxDINwzbxapWJ7MduObSs3Oef6w+tdChASYhOcoUW/5v3o07wPDUIaeLS+/Zn7Sc0r/w2Zb80HEzAgLDSs3L648DjiG8Z7tAapfZ7+Pvarn6EiIiJ1gEfDjLKOHDnC3LlzmTt3Lrt27XLc7KS/viIiIrj00ksZOXIkgwYN8kYZ4kF+84vY8T8cE3ymry/dFtoU+r4Obf7k8ZRg3jwYM8bxvLLvtpLb9eoFGzZU3N+5s2MYyQ03QEyMR0sTL1GYcWo2u41tqdtYc2iNM7xYf3g9+cX5VZ7bKaZTuck5+zTrQ8NQ33RNUhv7P4UZIiIidZvXwoyyNm3axOzZs1m4cCGZmZnlCzjxl1yTJk1YtmyZt0sRN5z8i1ibNm3q1y/xRZkEbX2GwD3/xTix1KKJQXHbmynqNgWCoz1+S6sVOnQIIzMTTNP1kCQoyGT0aBs331zM4MF29cKoZ6xWq/OPoNCTxxCdQWx2G0lpSaw9vJZ1R9ax7vA6Nh7bSF5RXpXndojuQJ9mfejTtA99mvXhrCZnER0a7f2iXaQ29n+ebuO8vDz27dvn/FhhhoiIiHtqPGdGdfTs2ZOePXvyyCOPsHTpUmbPns3y5cspLi52zq9x9OjR2ihFPMg0TWohC3OfaRKQMofgjQ9iKTjs3Gxr0IPCs/6FPeZs53GeNmtWIBkZricRjRvb+dvfivnLX4po0qR0e314maVUyfdFvfke8QCb3caO9B2sO7zOEVwcWcfGoxvJLcqt8tz20e3p3bS3M7jo3aR3pcFFXXotz8Q2PtN4uo31dSIiIuJZtRJmlAgKCuKSSy7hkksuIS0tjfnz5zN37ly2bt1am2WIhxiGUWHoUF1j5O4jeOO9BBxZ4txmBoRT1OVRijtMBksQ3vwMFiwIcM6NUWWthsmgQXbuu68YvFqVeFvJHEH14XukJuymnR1pO1h3ZB1rD69l/ZH1rD+y3qXgom3DtvRt1pfeTXs7HxuFNqqFqj3L39tYPN/G+joRERHxrFoNM8qKiYnhhhtu4IYbbiAxMZE5c+b4qhSpodDQ0Lo7zMReBNunwqanwVZmLH6LKzH6TyM4si21MfVsRgaUmQf3tEzTICsrkPBwn31bigf5y3wKdtPOjuM7yk3OuTZlLTmFOVWe2za6rXNizn7N+9GvRT9iwvxn4hd/aWM5NU+2sd3VHwYiIiLikjrxV1Pnzp35v//7P1+XIf7i2Ar443bI2FS6LawF9PsXtB5Xq8uAVOf3X4tFE3yKb9lNOzvTdpabnHNtylqyC7OrPLdNwzalwUULR3gRGx5bC1WLiIiIyJmoToQZIh5RmA7rH4Gd/y2z0YCEyXDWcxDk2eUaT8duh/feg59+qt45Y8d6ryaRskzTZFf6Lkdvi0NrWJ3iCC6yCrKqPDe+YXy55VD7tehHXHhcLVQtIiIiIuKgMEPqP9OEfV/A2nvAWmYi2UZ94ez/Qmz/Wi1nxw649VaozuI8hgHR0TB+vNfKkjOYaZrsTt/tHCayJmUNaw6tIbMgs8pzWzdoXW451H7N+9E4onEtVC0iIiIicmoKM6R+y94Jf9wJh78r3RYYCb2edfTIsNTel3hxMbz2Gjz1lGNJ1hLDhsEPPzieVzaZfcmol+nTQSs8irtM02RPxh5Hb4sy4UWGNaPKc1s1aOWc36J/C0d40SSiSZXniYiIiIjUNoUZUj/ZCmHby7D5WbAXlG5vNcYxN0ZE61otZ/16uPlmWLu2dFu7dvDOO44wY948mDQJ0tNxrm5S8hgd7QgyRo6s1ZLFD5imyd6MveUm51xzaA3p1vQqz20R1cI5TKTksWlk01qoWkRERETEfQozpP45uhxW3QFZ20q3hbeG/tOg1ahaLcVqhWefhX/+E2w2xzbDgHvucWyPiHBsGzUKDh2CmTNh5kwbaWkGMTEm48cHMn68emTUZ/sz95Oal1puW741H0zAgLDQsHL74sLjiG8YX+37mKbJ/sz9ztCi5DEtP63Kc5tHNi8fXLToR7PIZtWuQURERESkrlCYIfVHwXFY9xDs/qB0mxEAnf8OPadAUGStlvPLL3DLLZCYWLqte3fHxJ/nnFPx+NBQuPZaGDeusMxyf/oWrM/2Z+6n87TOWIutVR98QmhgKImTE08baJimyYGsA87JOUvCi+P5x6u8frPIZhWWQ20R1cLl+kRERERE6gP9JSV1n2nCnk9g3f1QUOYd8NizHRN8Nupdq+VkZ8Mjj8C//126LSgIHnvMsT04uFbLER9KzUutVpABYC22kpqX6gwzTNMkOSu5XG+L1YdWV+jtUZmmEU3LTc7Zv0V/BRciIiIickZQmCF1W1Yi/PFXOFJmjdOgBnDW89DxDrAE1Go5ixbBHXfAgQOl2wYOhPffd/TKEHHF0j1Lmb1ttnNyzqO5R6s8p3F4Y/q36F9uOdSWUS0xSmaQFRERERE5gyjMEN+xWWH/DEie4xhCEhLrmMAzfoJj/5YXYesLYC8sPSd+AvR9HcJr993n1FS491749NPSbeHh8I9/wN13Q0DtZipSz93/3f2n3d84vLFzGdSS8KJVg1YKLkRERERETlCYIb6RPA9WTIKidMAC2B2PB2Y5lloNioL8lNLjI9pC/39Dy+G1WqZpwpdfwt/+BseOlW4fNsyxUkm7drVajvihuPC4Csuhtm7QWsGFiIiIiMhpKMyQ2pc8D5aPKbPBXv6xOMfxD8AIhK73Q48nITC8FouE5GT4619hwYLSbdHR8NprjmVW9bem1NSNvW9kRMII+jXvR3zDeAUXIiIiIiLV5JUwY9u2baxZs4aUlBSysrKw2Ww8//zz3riV1Dc2q6NHBuBYu/I0jAC4dAXE9vd2VeXY7Y5eFw895Jjss8RVV8G0adBMK1qe8XIKc1iQtIB31rxTo/Mnnz2Zvs37ergqEREREZEzh0fDjG+++YZp06axa9cu57aSJShPDjNSU1MZM2YMNpuNfv36MW3aNE+WInXV/hknhpa4wLRB1vZaDTN27HAst7p8eem2Zs0cK5eMG1drZUgdlFOYw8KkhXy19SsW7VhU7VVMRERERETEczwWZjz55JPMmDEDcAQYVYmLi2PQoEHMnz+fH3/8kSNHjtC0aVNPlSN1VfIcSufIqIoFkmdDu2u9WxNQXOwYPvLUU2At8zfqzTfDyy9Do0ZeL0HqoJIAY8bWGSzasYj84nxflyQiIiIiIngozJg6dSpfffWV8+Pzzz+fc845h59//pmVK1ee8rwxY8Ywf/58TNNk2bJlTJw40RPlSF1WcBzXggwcxxWkebMaANavd4QWa9eWbmvXzjHUZNgwr99e6pjcwlwW7nAEGAuTFlYaYDSJaMJVXa+id7Pe3L7gdh9UKSIiIiJyZnM7zNi7dy/vv/8+AA0aNODNN99k4MCBAKSkpJw2zDjnnHMICwvDarWycuVKhRlngpBYqtUzIyTGa6VYrfDMM/DSS2CznbijBe65x7E9IsJrt5Y6Jrcwl0U7FvHV1q+qDDAmdJvAkDZDCLAEsDZlbSVXExERERERb3M7zPjyyy8pLi7GMAyeffZZZ5DhioCAADp37sz69evZuXOnu6VIfdBqjGP5VZfYodVYr5Tx88+OuTGSkkq39egB778PZ5/tlVtKHVMSYMzYOoOFOxaSV5RX4ZjG4Y0dAUZ3R4ARaNECUCIiIiIidYHbv5n//vvvAMTHx3PZZZdV+/yWLVuyfv16Dh8+7G4pUh/ET4DVf4eiDE6/mokBwdEQP96jt8/KgkcegbfeKt0WFASPPw4PPwzBwR69ndQxeUV5zgBjQdKCUwYY47qOY2L3iVUGGHHhcYQGhlZrMtDQwFDiwuNqVL+IiIiIiDi4HWYcOnQIwzDo1atXjc6PjIwEIDc3191SpD4ICIVB02H5aMCg8kDDcDycM91xvIcsWgR33AEHDpRuO+cceO896N7dY7eROsaVACMuPM45hGRo26Eu98CIbxhP4uREUvNSy23Pt+Y7vrQNCAsNq3Cv+IbxNf58RERERETEA2FGXp7jD4Pw8PAanW89sXRESEiIu6VIfdFqJAyZA79PgsJ0SufQOPEYHO0IMlqN9MjtUlMd82B89lnptvBweP55mDwZAgI8chupQ/KK8li8YzEzts5gftL8SgOM2LBY5xCSC9peUOMhJPEN4yuEE3l5ec5lqWv6f6OIiIiIiJya22FGdHQ0qamppKen1+j8/fv3AxAT472JHqUOajUKxh6C/TMdy68WpDkm+2w11jG0xAM9MkwTvvgC/vY3R6BR4pJL4L//daxYIv4jvyifxTsX89WWr1iQtIDcooq9vWLDYp1DSNwJMERERERExLfc/k0+Pj6eY8eOsXHjxmqfm56ezubNmzEMgy5durhbitQ3AaHQ7lrHPw9LToa//hUWLCjd1qgRTJ0K118PhuHxW4oPlAQYM7bOYH7i/NMGGBO6OXpgBAUE+aBSERERERHxJLfDjPPOO481a9Zw5MgRvv/+e4YNG+byue+88w5FRUUYhsG5557rbiki2O3wzjvw0EOQnV26ffx4ePNNaNbMd7WJZ+QX5fPNzm+cQ0hyCnMqHBMTFsO4LuOY0H0CF7a9UAGGiIiIiIifcTvMGDduHP/9738pLCxkypQpdOnShVatWlV53uzZs/noo48wDIMGDRowevRod0uRM1xSEtx6KyxfXrqtWTPHyiVjvbPCq9QSa7GVb3Z+w1dbvjptgDG2y1gmdp+oAENERERExM+5HWY0a9aMm266if/85z+kpqYyfvx4Jk+ezJVXXlnh2IKCAtauXcv//vc/vvvuO+cEeX/72980SZ7UWFERvPoqPP00FBSUbr/5Znj5ZcfwEql/SgKMGVtnMC9xXqUBRqPQRs4hJBe1u0gBhoiIiIjIGcIwTbOytTGrxTRN/v73v/Ptt99ilJmMICgoiMLCQgzDIDIykpycnHLnAIwdO5YXXnjB3RKkFuTk5JCYmOj8uHPnzs6ldX1l3TpHaLFuXem29u0dQ00uvth3dZ2OVro4NWuxlSU7lzgDjOzC7ArHNAptxNguY5nQfQIXt7u4TgYYamP/pzb2f55u47r4M1RERKQ+88hU/oZh8Prrr/Pmm2/yzjvvYLPZAJzzYQBkZ5f/oyQgIIC//vWvTJ482RMlyBkmPx+eecbR8+LElxsWC9x7r2O7/raoP6zFVr7d9S1fbfnqtAHGmC5jmNh9Ihe1u4jggGAfVCoiIiIiInWFx9YltFgs/P3vf2f8+PFMnz6d5cuXs3fv3grHNW/enAsuuICbbrqJ1q1be+r2cgb5+We45RbHHBklevSA99+Hs8/2XV3iuoLiApbscvTAmLt9bqUBRnRotKMHRrcJXNz+YgUYIiIiIiLi5LEwo0TLli159NFHefTRR8nIyODYsWNkZ2cTHh5ObGwsjRs39vQt5QyRlQUPPwz/+U/ptqAgeOIJ+L//g2D9rVunFRQX8O2ubx0BRuJcsgqyKhzTMKQhY7s6Aoxh7YcpwBARERERkUp5PMwoKzo6mujoaG/e4oxSVFTEypUrWbp0KStXruTAgQPYbDaaNWvG+eefzy233ELLli19XaZXLFwId9wBycml2wYNgvfeg27dfFeXnF5BcQHf7f6Or7Z8ddoAo2QIiQIMERERERFxhVfDDPGsP/74g5tvvhlwDNc577zzANi4cSOff/458+bN47333qNPnz6+LNOjjh2De+6Bzz8v3RYRAc8/D3fdBQEBPitNTqEkwCgZQpJZkFnhmJIAo6QHRkhgiA8qFRERERGR+kphRj1iGAaXXXYZN954Y7nAoqCggKeffppZs2Zx//33s2TJEoKC6t4KDyezWmHGDJgzB44fh9hYGDMGJkyAkBD43//g73+H1NTScy69FP77X2jb1kdFS6UKbYV8t8sRYMzZPqfSAKNBSANngHFJ+0sUYIiIiIiISI15ZGlW8T2r1cr5559PdnY2n3zyCWd7YSZMTy4rN28eTJoE6emOVUjs9tLHhg2hUydYvbr0+EaNYOpUuP56KLP6b73kL0s6FtoK+X73984hJBnWjArHNAhpwOjOo5nYfeIZFWD4SxvLqamN/Z+WZhUREanbXOqZcf3113u7DgzDYPr06V6/j78KDQ2lbdu2bNq0iaNHj/q6nNOaN8/RA6OE3V7+MTOzfJAxYQK8+SY0bVprJcoplAQYJT0wThdgTOg2gUs7XHrGBBgiIiIiIlJ7XAozVq1aheHFt8NL3vnwFJvNxq5du9i8eTNbtmxh8+bNbN++HavVCsDYsWN58cUXq33dH374gblz57J582aOHTtGZGQkbdq0YdiwYVx99dU+fYfFZrNx8OBBAOLi4nxWR1WsVkePDICq+gQZBnzxBUyc6PWy5DQKbYX8sPsHZmydweztsysNMKKCoxjdpTTACA0Mrf1CRURERETkjOHynBn1aTTKPffcw7fffuux6+Xm5vLAAw/w448/ltuelpZGWloa69at49NPP+X111+nd+/eHrtvdcydO5e0tDRiYmLo27evT2pwxYwZjqElrjBNKCz0bj1SuSJbET/s+YGvtnzFnO1zSLdWbLSo4ChGdR7FxO4TFWCIiIiIiEitcinM+Pjjj71dh0fZbLZyH5csEbt3794aXevvf/87P//8M+Do9TBhwgQ6duxIZmYmCxYsYO3ataSkpHDbbbfxv//9jw4dOnji03BZcnIy//znPwG49957CQ6uu0tbzplTOjdGVSwWmD0brr3W62UJpQHGjC2OHhiVBRiRwZHOISSXdbxMAYaIiIiIiPiES2GGNyaT9KZevXrRoUMHunfvTvfu3WndujWzZs3ikUceqfa1ZsyY4QwyOnbsyPTp08sN4/jLX/7CP//5Tz744AMyMzN58skn+eyzzyq91kMPPcTGjRurdf9LLrmE+++//5T7c3JyuPPOO8nIyODyyy9nYh0fk3H8uGtBBjiOS0vzbj1nuiJbET/u+dE5hCQtv+ILHhkcyajOoxwBRofLCAsK80GlIiIiIiIipfxyadY77rjDI9ex2WxMmzbN+fFLL71U6XwUDzzwACtWrGDbtm2sXr2aX375hfPPP7/CcSkpKezZs6daNRw7duyU+woKCvjrX/9KYmIigwYN4uWXX67WtX0hNrZ6PTNiYrxf05mmyFbET3t/4qstX50ywIgIinAOIVGAISIiIiIidY1fhhme8scffzjDhLPPPpvu3btXelxAQADXXXcdjz76KAALFy6sNMz45JNPPFZbUVERd999N6tWraJ379689dZbdXp4SYkxY2DWLNeOtdth7FivlnPGKAkwZmyZwazts04ZYIzsPJKJ3SZyecfLFWCIiIiIiEidpTDjNJYvX+58PmTIkNMeW3Z/2fO8wW638+CDD7Js2TK6dOnCO++8Q3h4uFfv6SkTJsDf/w4ZGadfzcQwIDoaxo+vrcr8T7G9mJ/2/MSMrTOYtW0Wx/OPVzimJMCY0G0CV3S8QgGGiIiIiIjUCwozTiMpKcn5vGfPnqc9tnHjxjRv3pyUlBRSU1OdK4t4mmmaPP744yxevJh27drxwQcf0LBhQ4/fx1tCQ2H6dBg92hFYVBZolKzSO32643hxXbG9mKV7lzqHkKTmpVY4JjwonJEJJwKMTlcQHlQ/gjAREREREZESHg8zsrOzWbNmDdu2bSM9PZ3c3FzsLkyQYBgGzz//vKfLcUvZ+S1atWpV5fGtWrUiJSUFgN27d3slzHjxxRf5+uuvadWqFdOnTyc2Ntbj9/C2kSMdq5pMmuRYprVkDo2Sx+hoR5AxcqSPC60nSgKMkiEkpwowRiSMYGK3iQowRERERESk3vNYmJGZmckrr7zC/PnzKSgoqNE16lqYkZ2d7XzeqFGjKo+Pjo6u9FxP+f777/noo48AaNmyJVOnTq30uGHDhjFs2DCP3/9kVqsVi8VSo3OHDYOdO2H27ADmzw8gLc0gJsZk5EgbY8faCA2FvDwPF1xHWK1WTNPEKOmCUgPF9mJ+PvAzs7bPYt6OeaTmVx5gXNH+CsZ2Hstl7S8rDTCKIK/IT1/cOsITbSx1m9rY/3m6ja1Wq0euIyIiIg4eCTOSk5O57rrrOHz4MObpJkI4wTCMCsfVxV8I88r8NR0SElLl8WWPyc3N9Xg9WVlZzucrV6485XEtW7aslTDDNE2X2vtUQkLg6quLufrq4kqu7U5ldceBrAMV5qooG/ad/HUVGxZL6watK71WSYAxO2k285IqDzDCAsO4vP3ljgCj3WVEBEc497nTVlI9Ja+1u98jUnepjf2fp9tYXyciIiKe5XaYYZomkydPdg6v6Ny5MyNHjuTXX39lxYoVzuEjubm5HDx4kNWrV7Np0yYAwsPDmTx5sku9HgTGjRvHuHHjfF2Gk2EYdTKEqisOZB2g9/u9KbC53lMpJCCEDbdscAYaxfZifjnwC7MTZzNnx5xKh5CUBBjjuoyrEGCIb5QEtvoe8V9qY//n6TbW14mIiIhnuR1mfPPNN2zfvh3DMDj//PP5z3/+Q2BgICkpKaxYsQKAsSetr7l582aefPJJtm7dyscff8z7779Phw4d3C3F48LDw8nMzAQc76YHBp7+5Sr7jntEhP//QRkaGlpvVlHxhdzM3GoFGQAFtgKybFmsPLKSr7Z8xaztsziae7TCcWGBYQzvNJyJ3SdyZacrFWDUQSV/BOl7xH+pjf2fJ9vYlfnDRERExHVuhxnff/894HjH4emnn67yD36AHj168Pnnn3PjjTeybt067rnnHmbOnOnSUI7aFBUV5Qwz0tPTqwwoMjIyyp0rUhOXfXoZ6db0CttDA0O5stOVTOg2gSsTriQyONIH1YmIiIiIiPhezWZvLGPjxo0YhkG3bt1o2bKly+eFhoby4osvEhAQwM6dO5k/f767pXhcu3btnM+Tk5OrPL7sMe3bt/dKTeL/ygYZoYGhjOs6ji+u+oJjDx5j5sSZ/KnHnxRkiIiIiIjIGc3tMCMtLQ2gwjCRsmNDT7W6SZs2bejTpw+mabJo0SJ3S/G4hIQE5/OSeT5OJTU11TlvSGxsrFeWZZUzQ3BAMGO7jOV/V/2PYw8e4+uJXyvAEBERERERKcPtYSYlQcXJ40nLDsnIyMigadOmlZ7fpk0bVq9ezZ49e9wtxeMGDx7M+++/D8Dy5cu59dZbT3nssmXLnM+HDh3q9drqAneWZj0T5Fvza3TegokLOK/VeY4PiiGvWMuo1jdattP/qY39n5ZmFRERqdvcDjMiIyPJzMys8EM6Ojra+Xz//v2nDDOys7MBOH78eKX7fenss8+mcePGHDt2jFWrVrFlyxa6d+9e4TibzcYnn3zi/Hj48OG1WabPaEnCKtTwpQkPDNfrWs9p2U7/pzb2f1qaVUREpG5zO8yIj49n06ZNHDt2rNz2jh07Op///vvvDBgwoMK5drudrVu3AhAWFuZuKR4XEBDAnXfeyZQpUwD4v//7P6ZPn05sbGy541555RW2bdsGQN++fRk8eHCt1+oLWpLw9DYc3VCzEw0t4VffadlO/6c29n9amlVERKRuczvM6NKlCxs3bmTXrl3ltvfu3Zvg4GCKior44osv+POf/1whBJg+fTrJyckYhkGnTp3cLcXpwIEDzJw5s9y2xMRE5/OtW7cyderUcvvPOeccBg0aVOFaEydO5Pvvv+fXX39lx44djB49mgkTJtCxY0cyMjJYuHAha9asAaBBgwY888wzHvs86jotzVq5pXuXMmXZFJbuXVqj88NCw/S6+gEt2+n/1Mb+T0uzioiI1F1uhxkDBw7kq6++4vDhwxw4cIDWrVsDjqVJL730UhYsWEBaWhpXXXUVN9xwAwkJCeTn5/Pjjz8yZ84c53U8OTTj0KFDvP3226fcn5iYWC7cAAgMDKw0zAgMDORf//oXDzzwAD/99BPHjh3jrbfeqnBcs2bNmDp1qkdDGak/TNPkp70/MWXZFJbvW+7rckRERERERPya22HG0KFDCQoKori4mG+++abcJJkPPvggP//8M1lZWRw5coSXXnqp0mt069aNCRMmuFuK10RGRvL222/z/fffM3fuXDZt2sTx48eJiIggPj6eSy65hKuvvpqoqChflyq1zDRNftjzA1OWTeGX/b+U29e6QWsOZB3wUWUiIiIiIiL+yyMTgL722mscP368wiSfTZs25cMPP+Tuu+/m4MGDlZ4/YMAAXn/9dYKCgtwtxWngwIEVel54wrBhwxg2bJjHryv1j2mafLf7O6Ysm8JvB34rty8hNoEnhjxBQmwCA98b6KMKRURERERE/JfbYQbAJZdccsp93bp1Y/HixXz77besWLGCo0ePYrFYaN26NRdeeCHnnXeeJ0oQqRWmabJk1xKmLJvC78m/l9vXJa4LTwx5gj91/xMBlgD2Z+4nNDAUa7Hry/GFBoYSFx7n6bJFRERERET8ikfCjKoEBwczYsQIRowYURu3E/E40zRZvHMxU5ZNYdXBVeX2dWvcjSeGPMGEbhMIsAQ4t8c3jCdxciKpeanljs+35juWbTUck32WFRceR3zDeK99HiIiIiIiIv6gVsIM8U9WqxWLxeLrMrzKNE0W71rMCyteYO3hteX2dYvrxsODHmZs57FYDAsF1oIK58cFxRHXsHxPC2uI1TlDfmhoaIVz8vLyPPtJSK2zWkvbWPyT2tj/ebqNrVbXe+mJiIhI1RRmSI2Zpolpmr4uwytM02ThroW8uOJF1h9ZX25fj8Y9eHjQw4zqNAqLYXEeX51rlzz66+t3plMb+z+1sf/zdBvr60RERMSzPBJm7Nq1i6KiIoKCgujQoUO1zwsJCaFdu3aeKEVqkWEYfveupN20s2DHAl5Y8QIbj24st69Xk148MugRRnQa4QwxasIwDOe7ff72+omD2tj/qY39n6fbWF8nIiIinuV2mHHw4EFGjhyJaZqMGTOGF154weVz33vvPebMmUNAQAA//vgjTZo0cbccqUWhoaGEh4f7ugyPsJt2Zm+bzTPLn2HjkfIhRp9mfXhq6FOM6jzKY7+MlvyC7C+vn1SkNvZ/amP/58k2ttvtHqhIRERESrg94cHixYudP6D//Oc/V+vca665BtM0sdlsLFq0yN1SRKrNbtqZsWUGZ719FuNnjC8XZPRr3o95V89jzW1rGN1ltN5VExERERERqSPcDjNWrlwJQOPGjenZs2e1zu3VqxeNGzcG4Pfff6/iaBHPsdltfLH5C3r+pycTZ05k89HNzn0DWgxgwTUL+OPWPxjZeaRCDBERERERkTrG7WEmO3fuxDAMunfvXqPzu3fvztKlS9mxY4e7pYhUyWa38eWWL3lu+XNsS91Wbt85rc7hqaFPcVmHyxRgiIiIiIiI1GFuhxlpaWkAzh4W1VVyXsl1RLyh2F7MF5u/4Lnlz5F4PLHcvnNbn8tTQ5/ikvaXKMQQERERERGpBzy2NGtRUVGNzisuLi73KOJJxfZiPt/0Oc8tf44daeV7/5wffz5PDX2Ki9tdrBBDRERERESkHnE7zIiJieHw4cMcPHiwRucnJycD0KhRI3dLkVpmtVqxWNyedsUriu3F/G/r/3h5xcvsythVbt/5rc7nkXMfYWj8UAzDID8/v1Zrs1qtzhnyxT+pjf2f2tj/ebqNrVarR64jIiIiDm6HGe3atSMlJYX169eTmZlJw4YNXT43MzOT9evXYxgGbdq0cbcUqWWmaWKapq/LKKfIVuQIMX5/mT2Ze8rtG9x6MI8MeoQh8UOc23xRf8k96+LrJ56hNvZ/amP/5+k21teJiIiIZ7kdZpx77rn89ttvFBUVMW3aNB577DGXz33zzTcpKirCMAzOPfdcd0uRWmYYRp15V7LQVshnmz/j5ZUvsy9zX7l9Q+OH8ui5j3J+6/N9VF15hmE43+2rK6+feJba2P+pjf2fp9tYXyciIiKe5XaYMXbsWP79739jtVr59NNPadq0KbfcckuV57377rt8+umnAAQHBzNu3Dh3S5FaFhoaSnh4uE9rKLQV8uG6D3nhlxcqhBiXtL+EJ4c+yfnxdSPEKKvkF2Rfv37iPWpj/6c29n+ebGO73e6BikRERKSE22FGbGwsN998M9OmTcMwDF599VW+//57rr32WgYOHFhulZPU1FR+//13PvvsM9avXw843qm48cYbadq0qbulyBmkoLiAD9Z9wAu/vMCBrAPl9l3W4TKeGvoUg1oP8lF1IiIiIiIi4k0eWc3krrvuYvv27Xz//fcYhsGGDRvYsGED4Oh1ER4eTl5eHoWFhc5zSsaOXnjhhdxzzz2eKEPOANZiK++vfZ8Xf32R5Kzkcvuu6HgFTw59knNaneOj6kRERERERKQ2eCTMMAyDf/3rX7z22mt88MEH5bpSFhQUUFBQUOEci8XCjTfeyH333eeJEqSe2J+5n9S8VJePjwuPI75hPPlF+by39j1e/PVFDmUfKnfMlZ2u5MmhT3J2y7M9Xa6IiIiIiIjUQR4JM8ARTjzwwAOMHz+eDz/8kF9++aXS5VpbtmzJkCFDuOGGG2jbtq2nbi/1wP7M/XSe1hlrsevL04UEhPB/5/0f7659l5ScL7mTPwAAN91JREFUlHL7RiaM5MmhT9K/RX9PlyoiIiIiIiJ1mMfCjBJt27ZlypQpABw/fpzU1FRyc3OJiIggLi6O2NhYT99S6onUvNRqBRkABbYCnln+TLltY7qM4ckhT9KneR9PliciIiIiIiL1hMfDjLJiY2MVXojHjOs6jieGPEHvZr19XYqIiIiIiIj4kFfDDPFvVqsVi8Xi8vH51vwa3efC+At54cIX6NmkJwB5eXk1uk5dYbVancv9iX9SG/s/tbH/83QbW63V65koIiIip6cwQ2rMNE3nqjSunVCz+zw79Fl6NO5RvXvVYSWfR7VfP6k31Mb+T23s/zzdxvo6ERER8Syvhxlbtmzh008/ZfXq1Rw7dozg4GCaN2/OkCFDuPbaa2natKm3SxAvMQyjeu9Y1fTNLQO/evfTMAznu33+9HlJKbWx/1Mb+z9Pt7G+TkRERDyrWmHGhx9+SFZWFgATJ06kefPmpz1+2rRpvPXWW+Xe1bBarWRnZ5OUlMRnn33GCy+8wGWXXVbD8sWXQkNDCQ8Pd/n4sNCwGt0nLDSsWvepD0p+Qfa3z0tKqY39n9rY/3myjcsuWy8iIiLucznMOHbsGP/85z8xDIMmTZpw9913n/b4Tz75hGnTpgGl7+CX7bJpGAZ5eXncf//9xMbG0r+/ltcUERERERERkaq5HGasWLHC+fyqq6467cSPR48e5bXXXnN2qTRNk/bt23PeeecREhLC9u3b+e233wAoLi7m6aefZsGCBTX9HERERERERETkDOJymLFx40bn80svvfS0x3722Wfk5+c7w4ybb76ZBx54oNx40VWrVnHHHXeQl5fHrl27WLFiBYMGDapu/SIiIiIiIiJyhnF5Xc2kpCQAoqOj6dKly2mPXbhwoTO46NGjBw8++GCFia/OPvtsHnroIefHP/zwg8tFi4iIiIiIiMiZy+UwIzk5GcMw6Nat22mPO3ToEMnJyc6Pr7/++lMeO27cOCIiIgDYtm2bq6WIiIiIiIiIyBnM5TAjIyMDgMaNG5/2uLVr1wKOeTIsFgsXXHDBKY8NDg6mZ8+emKbJ/v37XS1F6qm48DhCA0OrdU5oYChx4XFeqkhERERERETqI5fnzCgoKAAcy3GezubNmwHHCibt27cnKirqtMe3aNECgJycHFdLkXoqvmE8iZMTSc1LdfmcuPA44hvGe7EqERERERERqW9cDjPCwsLIzc0lOzv7tMdt2rTJ+byqISkAQUFBABQVFblaitRj8Q3jFU6IiIiIiIiIW1weZhITE4NpmuzcufOUxxQWFrJlyxbnZJ+9evWq8rpZWVkAhIeHu1qKiIiIiIiIiJzBXO6Z0aVLF/bv309SUhIHDhygdevWFY75+eefsVqtgGOYydlnn13ldQ8dOgRAXJzmRahvrFYrFovLeZicYLVaMU2zwgo/4j/Uxv5Pbez/PN3GJb8fiYiIiGe4HGacf/75fPvttwC88MILvPXWW+X2m6bJe++95/w4Pj6eTp06nfaahYWFbN26FcMwaNOmTXXqljrANE1M0/R1GfVOyWum189/qY39n9rY/3m6jfV1IiIi4lkuhxlXXnklr7zyCtnZ2fz000/ccsst3HjjjbRo0YLk5GTef/991q1bBzh6ZYwfP77Ka65atYqioiKXlnyVuscwDL0rWQOGYTjf7dPr55/Uxv5Pbez/PN3G+joRERHxLJfDjIiICB566CEef/xxDMPg119/5ddffy13TMkP/mbNmnHttddWec05c+Y4n/fv39/1qqVOCA0N1VwnNVTyC7JeP/+lNvZ/amP/58k2ttvtHqhIRERESlRrwoPx48dz1113Obtcntz10jRNGjRowBtvvEFYWNhpr3XkyBG+/fZbDMMgLCyMfv361ewzEBEREREREZEzSrVnb7z77rv55JNPGDJkCMHBwYAjxIiKimLMmDF8/fXXLq1i8s4771BYWIhpmgwePNh5LRERERERERGR0zFMN2akMk2T9PR0DMMgOjq6WuNBi4qKnL06AgICCAgIqGkZUktycnJITEx0fty5c2ciIyN9WFH9lJeXp+7pfk5t7P/Uxv7P022sn6EiIiKe5fKcGZUxDIOYmJganRsUFOTOrUVERERERETkDFXtYSYiIiIiIiIiIr7kVs8MObPYbLZyH+fl5fmokvrNarU6uy5rdnv/pDb2f2pj/+fpNj75Z+bJP1NFRESkerweZjz77LN8/vnnGIbB1q1bvX078aKCgoJyHx84cMBHlYiIiNRvJ/9MFRERkeqplZ4ZbswxKiIiIiIiIiJSjubMEBEREREREZF6RXNmiMuio6PLfRwSEqIldUVERFxgs9nKDS05+WeqiIiIVI/CDHFZcHAwTZo08XUZIiIiIiIicobTMBMRERERERERqVcUZoiIiIiIiIhIvaIwQ0RERERERETqFa/PmdGmTRsGDBjg7duIiIiIiIiIyBnCME3T9HURIiIiIiIiIiKu0jATEREREREREalXFGaIiIiIiIiISL2iMENERERERERE6hW3JwB95JFH3DrfYrEQGRlJVFQUHTp0oGfPnrRq1crdskRERERERETET7k9AWiXLl0wDMNT9QBw1llnceutt3LxxRd79LoiIiIiIiIiUv95JMyocFHD4HSXdWU/wNixY3n++efdKU9ERERERERE/IzbYcbs2bMBSElJ4e2336awsBCLxULfvn3p1asXzZo1Izw8nPz8fA4fPszGjRtZs2YNdrudkJAQ7rjjDuLi4sjIyCAxMZFly5aRnZ3tKM4wuOmmm3jwwQfd/0xFRERERERExC+4HWYAbNiwgdtuu42srCwuuugiHn30UVq2bHnK4w8dOsQLL7zAd999R3R0NO+88w69evUCIC8vj9dee41PP/0UgMDAQBYvXkzr1q3dLVNERERERERE/IDbq5lkZGTwt7/9jaysLMaOHcu///3v0wYZAC1atODNN9/kqquucp6fmZkJQHh4OI8//jgTJkwAwGazMXPmTHfLFBERERERERE/4XaYMWPGDI4cOUJERARPPPFEtc597LHHiIyM5MiRI8yYMaPcvnvvvZegoCAAVq1a5W6ZIiIiIiIiIuIn3A4zvv32WwzDYODAgYSFhVXr3PDwcAYOHIhpmixZsqTcvpiYGHr27Ilpmhw4cMDdMkVERERERETET7gdZiQnJwMQGxtbo/NLziu5Tllt2rQBcA5BERERERERERFxO8zIy8sDIDU1tUbnl5xXcp2ygoODAQgJCalhdSIiIiIiIiLib9wOMxo3boxpmqxcuZLc3NxqnZuTk8PKlSsxDIPGjRtX2J+VlQVAo0aN3C1TRERERERERPxEoLsXGDhwIMnJyeTl5fHMM8/wz3/+0+Vzn332WXJzczEMg7PPPrvC/p07d2IYRo2HsIj4Wk5ODr/++isrV65k69at7N27l+zsbEJCQmjSpAm9evVixIgRDB48GMMwfF2ueNjDDz/M7NmznR9PnjyZu+++24cVibu2bt3K/PnzWbFiBYcPHyYnJ4dGjRrRuHFjevfuzdlnn80ll1xCQECAr0uVakpOTmbmzJmsXLmS3bt3k5OTQ3BwMDExMXTt2pVLLrmE4cOHOycnFxEREd8yTNM03bnAxo0bufrqqym5zNChQ3n00UeJj48/5TkHDhzgH//4B8uWLcM0TSwWC1988QW9evVyHnPkyBEuuOACACZMmMAzzzzjTpkite7DDz9k6tSpFBQUVHls//79efnll2nRokUtVCa1YdmyZdx2223ltinMqL9ycnL4xz/+wezZs6nqx+Yff/xBgwYNaqky8YQPP/yQ1157jcLCwtMe165dO/71r3+RkJBQS5WJiIjIqbjdM6NXr17ccMMNfPjhhxiGwbJly1i2bBm9evWiV69eNG/enNDQUKxWK4cPH2bjxo1s3LgR0zSdvxDecMMN5YIMgK+//hrTNDEMg3PPPdfdMkVq3Z49e5xBRtOmTTn33HPp3r07sbGxFBQUsH79eubNm0deXh6rV6/muuuu46uvvlJPJD+Qk5PDU089BThWbapsTiCpPzIyMrj55pvZvHkz4Ph+vvTSS+ncuTNRUVHk5uayb98+fv31V7Zs2eLjaqW6Pv30U1588UXnx3369OGiiy6iefPm5OTksHPnTmbNmkVeXh579uzh+uuvZ/78+ZUOjxUREZHa43bPjBIvvPAC06dPL73wabrMl73l9ddfz6OPPlrhmM8++4z09HQAbrnlFkJDQz1Rpkiteeqpp0hOTuamm25i0KBBWCwVp6g5ePAgN998M3v27AFg3LhxvPDCC7VdqnjYk08+yZdffknz5s25/PLL+fDDDwH1zKivbr75Zn755RcAbrrpJu65555TTkx95MgRYmNjCQx0+70CqQVWq5Vzzz3XOefXc889x4QJEyocl5aWxg033EBSUhIAkyZN4pFHHqnVWkVERKQ8j4UZACtWrGDq1Kls3LixymN79uzJvffeq14X4rcyMjKIjo6u8rjt27czevRoAMLCwlixYgVhYWFerk68ZcWKFdx4442Ypsnbb7/N5s2bmTZtGqAwoz6aNWuW84/Wa665hqefftq3BYlH/fbbb9x4442A4/eSmTNnnvLYpUuXcvvttwPQvXt3Zs2aVSs1ioiISOU8+tbRoEGDGDRoEDt37mTlypVs376dtLQ08vLyCA8Pp1GjRnTt2pWzzz6bTp06efLWInWOK0EGQJcuXWjXrh179uwhPz+fffv20aVLF+8WJ16Rn5/PE088gWmaDB8+nAsvvNA5NEHqp3fffRdwDBd64IEHfFyNeNrx48edz9u0aXPaY8vu19AxERER3/NKP9iOHTvSsWNHb1xaxC9FRkY6n7syYajUTa+++ioHDhwgOjqaxx57zNfliJvWrFnD7t27Abj44ovLfZ+Kfyg7R9HevXtPe2zZ/XpDRkRExPcqDuIXkVpVWFhY7pdkrWhSP61du5bPPvsMgIceeoi4uDgfVyTu+uOPP5zPzzrrLAC+/fZbbr31Vs477zx69OjB+eefz2233cbXX39NcXGxr0qVGurXrx+NGjUCYPPmzcyYMaPS49LS0njttdcAsFgsTJo0qbZKFBERkVPQDGUiPrZgwQKys7MBxzhszZBf/xQUFPDoo49it9sZNGgQV111la9LEg8oO0QoNjaWu+++m2+//bbcMceOHXOu4vXRRx/x1ltv0bp169ouVWooJCSEKVOmcN9991FcXMzjjz/OrFmzyq1msmPHDmbPnk1ubi7h4eH84x//oF+/fr4uXURE5IzntTBjz549bN26lfT0dHJzc4mIiKBRo0Z069aNdu3aeeu2IvVKWloar7zyivPjv/71rz6sRmrqjTfeYM+ePYSGhvLMM8/4uhzxkGPHjjmf/+tf/2LPnj0EBQUxZswY+vXrR2BgINu3b2fmzJlkZGSQlJTEDTfcwKxZs1yeM0d877LLLuPDDz/kmWeeYceOHaxdu5a1a9eWOyYoKIg77riDq6++mubNm/uoUhERESnLo2FGTk4OH3/8MV988UW5XwJP1qRJE66++mquu+46jUGWM1ZhYSF33323cwK6YcOGcckll/i4KqmujRs38tFHHwFw9913Ex8f79uCxGMyMzOdz/fs2UPDhg356KOP6Natm3P7yJEjmTRpEpMmTWLnzp0cPHiQ1157TaFWPTNgwACeeOIJXnzxRbZu3Vphf1FREZ9//jn5+fncd999Wi5eRESkDvDYnBnr1q1j1KhRvPnmmxw9ehTTNE/578iRI/zrX/9i1KhRrF+/3lMliNQbdrudRx99lNWrVwMQHx/P888/7+OqpLoKCwt57LHHsNlsdO/e3bnEo/iHk1cuf+ihh8oFGSUaN27Mq6++6vx49uzZ5OTkeL0+8Yy0tDRuuOEGrr/+eg4ePMgjjzzC999/z+bNm1m9ejUfffQRQ4cOJSsri+nTp3PdddeRnp7u67JFRETOeB4JMzZv3szNN99MSkpK6YUtFtq3b8/gwYO59NJLGTx4MO3bt8diKb3loUOHuOmmm9iyZYsnyhCpF0zT5KmnnmL+/PmAY8LPDz/8kIYNG/q4Mqmu//znPyQlJREQEMCzzz5LQECAr0sSD4qIiHA+Dw8PZ9SoUac8tkuXLvTu3RtwhFxr1qzxdnniAfn5+fzlL39h5cqVNGzYkK+++opJkybRunVrgoKCiIqKYtCgQbzzzjv85S9/ARy9sZ577jkfVy4iIiJuDzMpLi7m/vvvd665HhUVxe233864ceOIiYmpcHx6ejqzZs3iv//9L9nZ2eTl5XH//fezcOFC/SEgfs80TZ5++mm++uorAJo1a8b06dNp1aqVjyuT6tq+fTvvvvsuAJMmTaJ79+4+rkg8rUGDBs7nCQkJBAcHn/b4Hj16OHsbHjhwwJuliYd8/vnnzuV3b7rpJtq2bXvKYx944AHmz59PVlYWixYt4uGHH9aEzSIiIj7kdpgxf/589u3bh2EYtG7dmg8//JCWLVue8vhGjRpx8803c/nll3PTTTexb98+9u3bx/z58xkzZoy75YjUWaZpMmXKFL744gsAmjZtyscff6w5FuqpWbNmUVRUhMViISgoiLfeeqvS48ou7/nHH384j2vXrh1XXHFFrdQqNdO+fXtWrFgB4NL8TmWP0TCT+mHp0qXO5+edd95pjw0PD6dPnz4sW7YMu93Opk2buOiii7xcoYiIiJyK22HGDz/84Hw+derU0wYZZbVs2ZJXX32VCRMmAPDdd98pzBC/VRJk/O9//wMck+B+/PHHtGnTxseVSU2VzKdgt9t5++23XTpn5cqVrFy5EoCLL75YYUYd16VLF+dzV8KJssdERUV5pSbxrKNHjzqfu9JmZY8p6ZEqIiIivuH2nBlbt27FMAzOOuusanez7tGjB2eddRamabJt2zZ3SxGpk04OMho3bszHH3982u7MIuJ7Q4YMwTAMAJKSkigsLDzt8Zs3b3Y+1xLk9UPZeVHKzvt1KocOHXI+1/K7IiIivuV2mFGyrGSHDh1qdH7JeSXXEfE3zzzzTIUgQ3/o1H+PPfYYiYmJVf6bPHmy85zJkyc7t59qWIrUHc2aNWPAgAGA4134efPmnfLY7du3O+fLiIiIoG/fvrVRorgpISHB+bxkUuZT2bdvHxs3bgQck5z36NHDq7WJiIjI6bkdZgQGOkaqVPWO1akUFRWVu46IP3n22Wf5/PPPgdIgo3379j6uSkRcdd999zmfv/TSS2zdurXCMampqTzwwAPOj6+77jpCQ0NrpT5xz4gRI5zPZ82axYwZMyo97tixY9xzzz0UFxcDcMEFF6hnhoiIiI+5nSDExcWVe7eiujZs2OC8jog/mTp1Kp9++ikAhmFw/fXXs3v3bufM+afSrVs3WrRoURslikgV+vTpw6233sq7775LZmYmEydOZOzYsfTr14/AwEC2bdvGzJkzycjIABzDJ++8807fFi0uO//887nssstYsmQJpmny+OOPM2/ePC6++GKaNm1KQUEBmzdvZu7cuWRlZQGO4SUPP/ywjysXERERt8OMfv36sW/fPvbv38/ixYurNaHdN99841wJpV+/fu6WIlKnrF271vncNE1effVVl8574YUXGDdunLfKEpFqeuCBBwgICODdd9+lqKiIr776yrm8clnnn38+r732GiEhIT6oUmrqlVdeITIykq+//hqAVatWsWrVqkqPbdeuHVOnTtXkzSIiInWA22HG8OHDmTVrFuAYQx4REcGQIUOqPO/XX3/l0UcfLXcdERGRuujee+/liiuuYObMmfz6668cOXKE4uJiYmNj6dOnD6NHj2bo0KG+LlNqIDg4mOeff57rrruOWbNmsXbtWpKTk8nJySEoKIiYmBh69OjhXIEoODjY1yWLiIgIYJgl6wu6YdKkSfz++++OCxoGF198MePGjaNPnz40atTIeVxGRgbr1q1j9uzZfPfdd5imiWEYnHPOOXz44YfuliEiIiIiIiIiZwCPhBlpaWn86U9/4sCBA46LnljKDiA0NJSwsDDy8/OxWq3O7SW3bdOmDf/73/+IiYlxtwwREREREREROQO4vZoJQExMDF988QWDBw8GHEFFyb/8/HzS0tLIz88vtx1gyJAhfP755woyRERERERERMRlHumZUdbvv//OV199xcqVKzl+/HiF/bGxsQwcOJA//elPDBw40JO3FhEREREREZEzgMfDjLKOHDlCeno6ubm5RERE0KhRI5o2beqt24mIiIiIiIjIGcCrYYYrfvrpJzIzMwEYM2aML0sRERERERERkXrA52HGmDFjSExMBGDbtm2+LEVERERERERE6gGPTADqLh/nKSIiIiIiIiJSj9SJMENERERERERExFUKM0RERERERESkXlGYISIiIiIiIiL1SqCvCxCR+i8lJYWPP/6Y3377jeTkZHJzc51z4Xz88ccMHDjQxxWKiL9LTk7m4osvBqBly5b8+OOPPq5IREREvElhhtQJ1113HatWrXJ+3Lx5c7799luCg4OrPPfNN99k2rRp/H979x4X07r/AfxTKlGKCCHXiE2hKGSzczkIx8axsd3KpeOWW245bfdE5Fau7W5HnVwT27XNdsslpbCFtCNKYVLTbdLUzPz+mN+ss6aZZqaapjqv7/v12q/XWjPPWvPMs2a1Pd/1PN8HAJycnLBv374aqyeR9ezZM8yfPx/5+fk1+jkCgQAPHz7E/fv38eTJE2RnZyMnJwdCoRBGRkYwMzODlZUVBg0ahKFDh0JXV7dG60OIppT/+1he48aNYWxsjC5duqB///6YOHEiWrVqpcEaEkIIIYRoHgUzSJ2UlZWFkydPYvbs2bVdFaKASCTC2rVrmUCGkZERBgwYgObNm0NbWzyLTR2dqkuXLsHPzw9paWly3+dwOOBwOHj+/DnCw8PRtGlTzJ49G3PnzkWjRo2q/fmEKMIOqC5duhRubm4a/Xwejwcej4esrCzExMTA398fCxcuxJIlS6ClpaXRuhBCCCGEaAoFM0iddezYMUyZMoU6o3XYs2fPmACDiYkJLl++DBMTE7Wdv6SkBB4eHrh8+bLU60ZGRrC2toaJiQkaNmyI7OxspKWl4d27dwAALpeLgwcP4unTpwgICFBbfQipbVZWVrC2tpZ6raCgAK9fv8abN28AAKWlpfDz80N+fj42bNhQG9UkhBBCCKlxFMwgdVZ2djZOnDgBV1fX2q4KqUBSUhKzPXz4cLUGMvh8PubOnYv4+HjmtT59+mD58uWwt7dHgwYNZI5JT0/H+fPnERISgqKiInz79k1t9SGkLhg6dGiFIz8SEhLg7u6OzMxMAEBoaCjGjx8PKysrTVaREEIIIUQjaDUTUuf06dOH2Q4MDERhYWHtVYYoxM6TYWpqqtZz+/j4SAUyXF1dcerUKQwaNEhuIAMAzM3NsWzZMty4cQOjRo1Sa30IqetsbGxw+PBhqaklp0+frsUaEUIIIYTUHJVHZkjmA6tbdnZ2jZyX1F9///vfkZeXh3fv3oHL5SIoKAjLli2r7WoROcrKyphtSY4MdYiPj8eJEyeY/enTp8Pd3V3l401MTHDw4EHcv39fbXUipD7o0aMH7OzsEBsbCwCIi4ur5RoRQgghhNSMSgUzKJEY0QRtbW0sW7YMK1euBACEhIRg1qxZaNasWZXPWZUl+4YNG4aPHz8CAG7evIl27dqpVOb9+/c4efIk7t27h6ysLJSWlqJjx45wcnLCnDlzZHKAvH37FmFhYYiLi8PHjx+hra2Nzp07Y8KECZg2bVqFoxCqIycnB2fPnsXdu3eRlpYGLpcLAwMDmJmZYeDAgZg8eTIsLCzkHhsZGQkPDw+Z1/39/WWCnlVNhsjOc2FmZoa1a9dW+hwA4ODgoPD9oqIinDt3Dnfu3EFKSgpyc3Ohr6+PVq1awc7ODhMmTEDv3r2Vfo6lpSWznZycDEB8XSMiIhATE4NPnz5BS0sL7dq1w9ChQ+Hi4lKpKTklJSX47bffcO/ePSQlJSEnJwd8Ph9NmjRBp06dYGNjg5EjR8qt6/r163H+/HkAgLe3NyZNmqTws9jXd+LEidi5c6dKZQQCAa5du4ZLly7hzZs34HA4KCkpwaFDhzBixAjExsYyCX3t7OyYYNWdO3dw4cIFvHjxAhwOBzweDx4eHnB2dpb53NTUVFy4cAEPHjxAZmYm8vPzYWhoCHNzcwwePBjTpk1TmnCWvTKIZNlgLpeL06dP4/r168jIyEBxcTFMTU1hb28PZ2dndOvWTem5JOTdB4rasib06NGDCWZ8+fJFpWPU0baAOH/HnTt38PjxY7x69QofPnxAUVER9PT0YGJiAmtra4wYMQKjR4+uVAD0y5cvCA8Pxx9//MH8zTUzM4ODgwOmTZuGzp07q3wuyfc9d+4c4uPj8f79exQVFUFLSwuGhoYwMzODpaUl7OzsMGzYMBgbG1fq3IQQQgjRjErlzBCJRDVVD0KkjBkzBseOHcPr169RVFSEgICAKndoNenChQvYtGkTiouLpV5PTk5GcnIyrl+/jpCQEOYfx4cPH4afnx+EQqFU+WfPnuHZs2e4du0ajh8/rtYkqGfPnsXOnTtRUFAg9TqXywWXy8WrV68QGhqKmTNnYt26dTUSTFEkMzMTd+7cYfanTp2Kxo0bq/1zbt26hV9++QUcDkfqdT6fj/z8fKSkpCA8PBzjxo3D9u3bK3UNIiIisGPHDvD5fKnXJb+D06dP49dff1Upl0F0dDS2b9+Oz58/y7yXk5ODnJwcPHnyBAEBAdi8eTOmT5+ucj3V5fPnz1i5ciWePHmi8jEFBQXw8PDA77//rrQsn8/H9u3bcfbsWQgEAqn3cnNzkZubi+fPnyMoKAhr1qzBzJkzVa7HkydPsHLlSpn2zcjIQEZGBqKiorB582b89NNPKp+ztunr6zPb5X+D5amzbaOjo+Hu7i73M0tLS1FUVIT09HRcvnwZx44dg7+/P8zNzZV+n99//x0bNmyQWf75r7/+wl9//YWIiAhs3LgRAwcOVHouQLz6zJEjR2S+L/DfeyopKQmRkZEYP3489uzZo9J5CSGEEKJZKgcz+vfvX5P1IESKlpYWli9fjkWLFgEAwsPD4ezsjJYtW9ZyzSp29+5dbNu2DUKhEB07doSVlRUaNmyI5ORk/PnnnwCAly9fYtWqVQgMDMSxY8dw4MABAOIn+927d0eDBg3w559/IiUlBQDw+PFjeHt7Y+vWrWqpY2BgIHx8fJh9PT092NnZwczMDPn5+YiNjQWXy4VAIEBoaCiysrJw8OBBqVFZXbp0wYwZMwAAz58/Z76bvFUWyu+rIjY2VipwOm7cuEqfQ5krV65g9erVTGemQYMGsLW1Rfv27cHj8RAfH8880b506RI+fvyI0NBQNGzYUOm5IyMjsXnzZgBAp06d0KtXL+jr6+Pt27dISEiASCQCl8vFokWLcPXqVTRp0qTCcwUFBcHHx4dpDy0tLVhaWsLCwgIGBgbgcrl48+YNs4pLSUlJdZqlSvh8PhYtWoSkpCTo6Oigb9++MDc3B5/Px8uXL+UeIxKJsGbNGty6dQtaWlro1asXLCwsIBKJkJKSIvV74/F4mDdvHhISEpjX2rdvj549e8LIyAh5eXlISEjAly9f8O3bN2zbtg2FhYVYuHCh0rqnpKTA19cXPB4PzZs3R79+/dC0aVN8/vwZjx49wrdv3yAQCLBp0yZ069ZNKp8PAIwYMQJdu3ZVeh8AUGmEj7qwR2M0b968wnLqbtuvX78ygYzWrVvDwsICLVq0gL6+Png8HlJTU/Hy5UuIRCK8fv0aM2fORFRUlMJRd7dv38aKFSuYKW3a2tqwsbFBx44dwePxEBcXBw6HA09PT3h6eiptm9DQUKmRM82aNUOfPn1gamoKLS0tcLlcvHv3DqmpqXKDHYQQQgipO1QOZrDnrxOiCcOGDUPv3r3x7NkzfPv2DUePHsXGjRtru1oV8vb2RqNGjbBjxw6MHj1a6j125zkmJgYhISE4cOAAWrZsCV9fX9jZ2UmVDw4OZoaknzlzBq6urnKnuVRGQkICfH19mf0hQ4bA29sbLVq0YF7j8/nYv38/AgMDAYiftIaEhMDFxYUp07t3b6Zj5ufnx3TiFK2yUBnspJ/NmzdX6cltZXz48AH/+te/mI6KtbU19uzZgw4dOjBlhEIhQkND4ePjA6FQiMTEROzevVulztKmTZtgYmKCXbt2YciQIVLvxcXFYeHChSgsLASHw0FoaCiWLl0q9zx37tyRCmQMGDAAGzduRJcuXWTKpqenIzIyslaGw1+/fh1lZWWws7ODt7e3zO9U3lP6xMRElJWVoVu3btizZ4/UNJ3yx2zZsoXpbHfs2BFbt26Fvb29VHmBQIBTp07B29sbfD4fBw8ehL29Pfr27auw7rt27YJAIMD69esxa9Ys6Oj893+JWVlZcHV1xZs3byAUCrF37178+9//ljp+zpw5AGrmPqiqsrIyPHz4kNlXFERRd9u2atUK7u7uGDVqlNT9xJaeno7Nmzcz06/27NkDLy8vuWVzc3OxYcMGJpDRrVs37N+/X+oeEAqFCAwMhK+vL3bt2lXhdwXEbXPkyBFm393dHS4uLtDV1ZUpy+VycfPmTeTk5Cg8JyGEEEJqD61mQuq0FStWMNunT59m5krXRaWlpfD395cJZACAk5OTVK4Cb29v6OrqIiQkRCaQAQAuLi4YNGgQAPE/1q9evVrt+u3du5fpwPft2xeHDh2SCmQA4pEaa9euxaxZs5jX/P39NbqiDPsay+u4V9ehQ4fA4/EAAB06dEBQUJBMx0tbWxsuLi5Yt24d81p4eDjS09NV+ozg4GCZQAYgHuG2atUqZv/y5ctyjy8rK8OWLVuYQIajoyMCAwMrbA9zc3MsX74cEydOVKl+6iQJSgQEBMgNuOnp6ck9xtTUFKGhoTKBDPYx8fHxiIqKAiAeMRARESHT2QbEI2t+/vlnbNmyBYC4A37o0CGldefz+di0aRNcXFykAhmAOB+Dr68vM0rk8ePHKuefqE3Hjx9HVlYWsz916lS55WqibYcNGwZXV9cKAxmA+Ld69OhR5rr/9ttvyMvLk1s2JCQEX79+BQC0aNECISEhMveAtrY2FixYgOXLl6O0tLTCzwXEeWxyc3MBiFd+cXV1lRvIAICmTZti8uTJWLBggcJzEkIIIaT2UDCD1GmDBg1iOvulpaUqdVBqy7Bhw5gAhDxjx46V2p86darCzjq7vOSpb1WlpqZKrWqwceNGuZ1MiVWrVjFDvwsLC3Hp0qVqfX5lsDs2RkZGaj13fn4+rly5wuyvWbNG4TSP2bNno2vXrgDEQSVVlrmcOnUqunfvXuH7EyZMYDrO7969kxsoio6OZoI6jRs3xo4dO2Q623XJ6tWrpfI0qGLx4sVKk6AGBwcz2+vWrVNaftKkSUwiyJiYGKbjWpFu3bpV2NmXvC/JayISifDixQuF56sthYWFiI+Ph7u7OzN1DQCcnZ0rTIJb022riK6uLsaPHw9APDVKXq4VkUiEc+fOMfuLFy9WOGVm/vz5aNu2rcLPZd9rlUnASwghhJC6iYIZpM5jj86IiopCWlpardVFkVGjRil8v/wTaGXl2SsoZGRkVL1iAB49esRs9+jRA999953C8o0bN5bKVSFZGUETioqKpOqhTomJicwUhmbNmsHR0VFheW1tbUyePJnZV6Ud5I3MYZOsEAGIO2zyRhvdu3eP2R47dmyd7ngZGxtj8ODBlT7OyclJ4ftlZWV48OABAHGbKbtWEpLRBSKRSCoXhDzKrhUgvl8k6sLIMH9/f1haWkr9Z2trixkzZjBBx6ZNm8Ld3V3uqkOAZto2Pz8fd+/eRVBQEPbu3Yvt27dj69atzH/sZZNfvXolc3xqaiqTnFdHR4cJflREV1dXaX4dMzMzZjs2NpbJNUMIIYSQ+qnuPuoj5P/Z2tpiyJAhuHv3LgQCAfz8/KRyP9QVFS3fKFF+lIHkiX9F2PkPqjvNg91ZUJZHQMLGxobJlVNRIseaYGBgwGxLpoOoC/t7WFtbqzTawcbGRup4kUikcJlqZb8DQNzZlJB3bZ8+fcpsyxv6X5dIEtdWRrt27aTaQJ7k5GTm+uvo6FSYV6E89iimT58+KSwrb4pLeezklJqcblVVDRo0wOrVqzFlypQKy9Rk20ryYFy/fl3pSioS8kZ5sO/Vzp07qzRKq3yC1vLMzMzQp08fPH36FAUFBZg0aRImTJiAkSNHwsbGRq2rRhFCCCGk5lEwg9QLK1aswL179yASiXDlyhW4urqq1BHRJENDQ4Xvl+84K5reAECqgyhJgFdV7CR2bdq0UekY9pDt6gwpryx2EKf8UozVVd12kCwvqehaK7uuAKTm6cu7tpI8AQDUngBV3aoyakSVY9j5KbhcLsLDwyv9ORXlYpBQds8C0vdtde9DdSi/WgqPx0NmZiYz6kggEMDT0xMZGRlYuXKl3HPUVNu+fPkSzs7OStu9PPZoLAn2vcoeUaGIKve0l5cX5syZg+zsbPB4PERERCAiIgI6Ojro3r07+vfvj8GDB2PgwIEaX5aaEEIIIZVDwQxSL/Ts2RMjR45EdHQ0hEIhDhw4gMOHD9d2taQoelqvjvLVwR7hoOrUDfZTSnmdjZrCDh6kpqaq9dzsdlD1KWz5csqCGeq4rjU51UbdKpsrQ9VjCgoKqlIdKcqW1tTkPaguFa2WwuFwsHPnTmaqiSTJprzpPDXRtnw+H25ubkwgw8TEBFOnTsXAgQPRoUMHGBsbQ19fn2nzyMhIZhoMeylmCfa9qupvTJV72sLCAhcuXMDRo0cRFRXFtEVZWRlevHiBFy9eIDg4GK1atYKbm5vCES6EEEIIqV0UzCD1xrJly3Djxg0IhULcvHkTz58/l3pCqW5CobDGzq1p7A6xqlM3iouLmW321I+aZmtri7NnzwIQj1DIyMio9rK0Eux2YH8/RcqX00RbGBgYMJ1CdU+1Uaau/O7Z18rS0hIXL16sxdrUfaampti9eze4XC5iYmIAiJdedXBwkFmytyba9vr160xun1atWuHs2bNo2bJlheWVBUjZdfz27ZtKdVD1nm7RogU8PT2xdu1aPH36FPHx8UhMTERCQgIzlejz58/w9PREcnKySksyE0IIIUTzKAEoqTe6du0qleCNnbVfGWXD+uVRx9PLuoI9rJ+9bKMi7GSH7LwBNc3e3l7qibk6V1Kpbjvo6upqJJjBXrWhuslf2dMklI1UAOpOXgh2G2RnZ9diTeoPbW1teHl5MYEALpeLo0ePypSribZ9+PAhsz1nzhyFgQwAyMzMVPh+Ve5VVctJ6Onpwc7ODosXL0ZAQAAePXqEgIAA2NraMmVOnDiB58+fV+q8hBBCCNEMCmaQesXNzY3pnMXExEgtN6oIuwOan58vd1gzW2ZmZp3p1KkDe0WGxMRElY5hr1agbPUTdWrbti2GDBnC7J86dUrlJ67KsL/H8+fPVercs9vru+++08jUBHYiQ/ZKNFXBnhKjSu6T5OTkan2euvTo0YNZPvjr1694//59LdeoYnVpukrr1q0xe/ZsZj88PJxZFUSiJtqWnYdDlSS4yv52s+/Vt2/fqhRcZifOrQpdXV0MGTIEISEhUt/h1q1b1TovIYQQQmoGBTNIvdK+fXtMmjSJ2d+/f79KxxkaGjKrJxQXFytdku/q1atVrWKdNGDAAGb75cuXeP36tcLyxcXFuHLlitzjNcHV1ZXZzszMxJ49e6p0Hvbyj4B4JRdJJy4nJwe3b99WeLxQKMS5c+eYfU21w/fff89sX758WSoZYmWxc5Aou+4lJSV1puOmr68v1d7/+c9/arE2ikl+U0DdSBI6d+5cJoBbUlKCX3/9Ver9mmhbbe3//nNC2bSQFy9eSK2MIk/nzp1hamoKQNymykZoqVJGVXp6enBwcGD22Ql5CSGEEFJ3UDCD1DuLFy9mOg/x8fHM/HBl2Pk1zp8/X2G5T58+4fjx49WrZB3TpUsX9O/fn9nftm0bSktLKyy/f/9+5h/whoaGUtN7NKFfv374+eefmf2wsDCVA1eAeATCsmXLZIbYGxkZSSVE9PHxUTgCJywsDG/evAEg7qz99NNPKtehOv72t78xQQgej4cNGzZUuZPcu3dvZvvWrVsKAyMHDhzQ6Mo1yixYsIDZDgsLw4MHD1Q+tvxohJrEnob1+fNnjX1uRYyNjTFr1ixm/9SpUzLXXd1ty151548//qjw2OLiYmzcuFHpZ2hra2Py5MnM/qFDhxT+doOCgpROycrLy1M5Jwx7ykpVVuwhhBBCSM2jYAapd8zMzDB16lRmX9WhxewOeXBwMK5fvy5T5unTp5g5cyby8vKk8mz8L1i1ahWz1GB8fDzc3Nxknjjy+Xz4+voiJCSEeW3p0qUaTQAq4eHhgb59+zL7R44cwfTp0/Hw4cMKp4ekp6fj4MGDGD58uNzrCwBLlixhcgqkpaVh/vz5SE9PlyojFAoRGhqKnTt3Mq/NmDFDbYlIldHR0cEvv/zCTF+4desW5s2bV+HqLhkZGThw4ACioqJk3rOyskL79u0BiAMj7u7uMktnFhcXY9euXQgMDJQaZVDb7OzsMHHiRADiJ++urq44duxYhckjS0pKcOPGDSxatAiLFi3SWD27du3KbMfExNSJfDvOzs7M77y4uBhBQUFS76u7bR0dHZnt8+fPIygoSOY+ff/+PebOnYukpCSVVumZM2cOEyjicDhwcXGRuQeEQiGCgoKwb98+pX+zb968iVGjRiEwMLDCwAefz0dYWJjU3w/2tDdCCCGE1B20mgmplxYuXIizZ89WKpfC2LFjERQUhNevX6O0tBTLli1Dz5490b17dwiFQiQnJ+Ply5cAxLk5IiMjpZI/1nc2NjZwd3eHj48PAHEH+YcffoC9vT3MzMyQl5eH2NhYcLlc5piRI0fC2dm5Vuqrp6eHkJAQrFu3DteuXQMgzuPh7OwMY2NjWFlZoXnz5tDT00N2djbS0tJkpg/JC8K0b98eXl5eWL16NQQCARITEzF69GjY2tqiffv24PF4iI+Pl3rC3qdPH6xZs6Zmv3A5jo6OWLVqFXx9fQGIc2eMHTsW3bt3h4WFBRo3boy8vDwkJycz31uy1CWblpYWVq1ahRUrVgAAHjx4gOHDh2PgwIFo1qwZOBwO4uPjkZ+fj5YtW2LGjBnYt2+fxr6nMlu3bgWHw0FMTAxKS0uxd+9eHDlyBNbW1mjTpg309PSQn5+PDx8+ICUlBXw+H4B4OWdNsba2hpmZGbKyssDhcDBmzBg4ODigWbNmTEDKyspK7jKpNaVZs2aYMWMGAgICAIhzZ8yfP5+Zbgeot20HDx6M/v37Iy4uDiKRCLt27UJ4eDh69uwJQ0NDvH//HomJiRAIBGjVqhVmz56N3bt3K/wOJiYm8PLygpubGwQCAV6/fo1x48bB1tYWHTt2BI/HQ1xcHJOvw8PDA15eXgrP+eHDB/j4+MDHxwdt2rSBpaUlM/IiOzsbz549k/obOH78eNjY2Chtb0IIIYRoHgUzSL3UokULzJo1q1LTQXR0dODv7w8XFxfmSXxSUhKSkpKYMlpaWvjnP/+JJUuWIDIyUu31rm3z5s2DkZERdu7cicLCQvD5fNy7d0+mXIMGDTBjxgysX7++VpMb6uvrY//+/bh48SIOHTrEJCrMy8tTOL3I1NQULi4uUkPt2ZycnNCoUSN4enoiOzsbZWVliI2NRWxsrEzZcePGYfv27WjYsKF6vlQluLq6ol27dvDy8kJ2djZEIhFevXqFV69eyS1f0dPuMWPGIDU1FX5+fgDEK/VER0dLlenUqRP8/PyU5jLQND09PRw/fhz+/v4IDg5GcXExiouL5V4rCV1dXakkqjVNW1sbmzZtgpubG0pLS8HhcGRGyUycOFGjwQxAnDsjPDwcPB4PPB4PISEhTFALUH/b7t+/H66urszf1IyMDJkREBYWFjhw4IDKK4QMHz4ce/fuhaenJwoKCiAUChEXFyeVQFRPTw+enp5wcHBQGMxo3LgxtLS0mATQmZmZFa6qoq2tjWnTpmHDhg0q1ZMQQgghmkfBDFJvzZ8/HxEREZUa0m1ubo6LFy8iLCwM0dHRSEtLA5/PR8uWLdGvXz9Mnz5dKsfA/6IpU6Zg+PDhOHPmDO7evYu0tDTk5eXBwMAArVu3xqBBgzB58mRYWFjUdlUBiANMEyZMwNixY/Hw4UPcv38fT548AYfDQW5uLoRCIYyNjdGuXTv06tUL33//PQYPHsxMqamIo6MjoqOjce7cOdy+fRspKSnIzc2Fvr4+WrZsCXt7e/z444+1/ntwcnLCDz/8gKioKNy9exfJycnIycmBQCCAsbExOnXqBFtbW4waNUrhqjNLly6Fg4MDwsLCEB8fj69fv8LQ0BAdOnSAk5MT/vGPf8DAwKDOBTMAcXBt+fLlmDVrFqKiovDgwQOkpqYiNzcXZWVlMDAwQNu2bdGtWzfY29tj6NChGs9z4OjoiHPnziE8PBwJCQnIzMwEj8dTunJSTTIxMcG0adOYKSZhYWGYO3cujIyMmDLqbNsWLVrg5MmTOHPmDC5fvoyUlBQUFxejefPm6NSpE5ycnDB+/Hg0atSoUsudjh49Gn379kVYWBhu3bqFjx8/QktLi/l7NX36dHTp0kVpzozRo0cjJiYGMTExSEhIQHJyMtLT05Gfnw8AaNKkCTp27AhbW1v8+OOPdeZvICGEEELk0xLV5r+0CCGEEEIIIYQQQiqJEoASQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuoVCmYQQgghhBBCCCGkXqFgBiGEEEIIIYQQQuqV/wM9tuMkkeApMwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_context(\"poster\")\n",
+    "# Read the CSV file\n",
+    "df = pd.read_csv('./format_comparison_results.csv')\n",
+    "\n",
+    "# Define colors and markers for each format\n",
+    "format_styles = {\n",
+    "    'LEROBOT': ('red', '^'),\n",
+    "    'RLDS': ('purple', 'D'),\n",
+    "    'Fog-VLA-DM': ('blue', 'o'),\n",
+    "    \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n",
+    "    'HDF5': ('green', 's'),\n",
+    "}\n",
+    "\n",
+    "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n",
+    "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n",
+    "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n",
+    "\n",
+    "# Update the format_styles dictionary\n",
+    "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n",
+    "\n",
+    "# Get unique datasets and batch sizes\n",
+    "datasets = df['Dataset'].unique()\n",
+    "\n",
+    "# Create a figure for each dataset\n",
+    "for dataset in datasets:\n",
+    "    plt.figure(figsize=(6, 6))\n",
+    "    \n",
+    "    dataset_df = df[df['Dataset'] == dataset]\n",
+    "    \n",
+    "    # Create the line plot\n",
+    "    for format, (color, marker) in format_styles.items():\n",
+    "        data = dataset_df[dataset_df['Format'] == format]\n",
+    "        plt.plot(data['BatchSize'], data['AverageLoadingTime(s)'], \n",
+    "                 color=color, marker=marker, label=format, linewidth=2, markersize=8)\n",
+    "\n",
+    "    # Customize the plot\n",
+    "    # plt.xlabel('Num of Concurrent Reads')\n",
+    "    # plt.ylabel('Log-Scale Average Loading Time (s)')\n",
+    "    plt.title(f'{dataset}')\n",
+    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    # plt.xscale('log')  # Use log scale for x-axis\n",
+    "    plt.yscale('log')  # Use log scale for y-axis\n",
+    "    plt.tight_layout()  # Adjust layout to make room for the legend\n",
+    "    \n",
+    "    # Add a grid for better readability\n",
+    "    plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.savefig(f'./{dataset}.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "443c3736",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3200483/2817297649.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Summary for berkeley_autolab_ur5:\n",
+      "                         mean    median       min       max\n",
+      "Format                                                     \n",
+      "Fog-VLA-DM-lossless  2.824063  3.084723  1.922030  3.465437\n",
+      "HDF5                 4.259725  4.163264  4.081820  4.534092\n",
+      "LEROBOT              0.658879  0.640482  0.628601  0.707555\n",
+      "RLDS                 1.571795  1.508707  0.726021  2.480656\n",
+      "\n",
+      "Fog-VLA-DM-lossless:\n",
+      "  On average, Fog-VLA-DM is 2.82x faster\n",
+      "  Median speedup: 3.08x\n",
+      "  Range: 1.92x to 3.47x faster\n",
+      "\n",
+      "HDF5:\n",
+      "  On average, Fog-VLA-DM is 4.26x faster\n",
+      "  Median speedup: 4.16x\n",
+      "  Range: 4.08x to 4.53x faster\n",
+      "\n",
+      "LEROBOT:\n",
+      "  On average, Fog-VLA-DM is 0.66x faster\n",
+      "  Median speedup: 0.64x\n",
+      "  Range: 0.63x to 0.71x faster\n",
+      "\n",
+      "RLDS:\n",
+      "  On average, Fog-VLA-DM is 1.57x faster\n",
+      "  Median speedup: 1.51x\n",
+      "  Range: 0.73x to 2.48x faster\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Summary for berkeley_cable_routing:\n",
+      "                         mean    median       min       max\n",
+      "Format                                                     \n",
+      "Fog-VLA-DM-lossless  0.809255  0.792606  0.714179  0.937631\n",
+      "H264                 1.310345  1.283263  1.231549  1.439083\n",
+      "HDF5                 2.261303  2.398626  1.886863  2.435957\n",
+      "LEROBOT              0.031114  0.031281  0.028841  0.034557\n",
+      "RLDS                 0.073306  0.079867  0.022246  0.123708\n",
+      "\n",
+      "Fog-VLA-DM-lossless:\n",
+      "  On average, Fog-VLA-DM is 0.81x faster\n",
+      "  Median speedup: 0.79x\n",
+      "  Range: 0.71x to 0.94x faster\n",
+      "\n",
+      "H264:\n",
+      "  On average, Fog-VLA-DM is 1.31x faster\n",
+      "  Median speedup: 1.28x\n",
+      "  Range: 1.23x to 1.44x faster\n",
+      "\n",
+      "HDF5:\n",
+      "  On average, Fog-VLA-DM is 2.26x faster\n",
+      "  Median speedup: 2.40x\n",
+      "  Range: 1.89x to 2.44x faster\n",
+      "\n",
+      "LEROBOT:\n",
+      "  On average, Fog-VLA-DM is 0.03x faster\n",
+      "  Median speedup: 0.03x\n",
+      "  Range: 0.03x to 0.03x faster\n",
+      "\n",
+      "RLDS:\n",
+      "  On average, Fog-VLA-DM is 0.07x faster\n",
+      "  Median speedup: 0.08x\n",
+      "  Range: 0.02x to 0.12x faster\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Summary for bridge:\n",
+      "                         mean    median       min       max\n",
+      "Format                                                     \n",
+      "Fog-VLA-DM-lossless  1.401418  1.319205  1.136809  1.830454\n",
+      "H264                 1.708113  1.538449  0.955733  2.698478\n",
+      "HDF5                 2.291325  2.065455  1.412598  3.823695\n",
+      "LEROBOT              0.242532  0.233347  0.198193  0.309825\n",
+      "RLDS                 0.180912  0.138910  0.046215  0.416763\n",
+      "\n",
+      "Fog-VLA-DM-lossless:\n",
+      "  On average, Fog-VLA-DM is 1.40x faster\n",
+      "  Median speedup: 1.32x\n",
+      "  Range: 1.14x to 1.83x faster\n",
+      "\n",
+      "H264:\n",
+      "  On average, Fog-VLA-DM is 1.71x faster\n",
+      "  Median speedup: 1.54x\n",
+      "  Range: 0.96x to 2.70x faster\n",
+      "\n",
+      "HDF5:\n",
+      "  On average, Fog-VLA-DM is 2.29x faster\n",
+      "  Median speedup: 2.07x\n",
+      "  Range: 1.41x to 3.82x faster\n",
+      "\n",
+      "LEROBOT:\n",
+      "  On average, Fog-VLA-DM is 0.24x faster\n",
+      "  Median speedup: 0.23x\n",
+      "  Range: 0.20x to 0.31x faster\n",
+      "\n",
+      "RLDS:\n",
+      "  On average, Fog-VLA-DM is 0.18x faster\n",
+      "  Median speedup: 0.14x\n",
+      "  Range: 0.05x to 0.42x faster\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Summary for nyu_door_opening_surprising_effectiveness:\n",
+      "                         mean    median       min       max\n",
+      "Format                                                     \n",
+      "Fog-VLA-DM-lossless  1.512650  1.533295  1.275668  1.708343\n",
+      "H264                 1.374171  1.363077  0.893099  1.833454\n",
+      "HDF5                 1.598478  1.512395  1.357568  1.887998\n",
+      "LEROBOT              0.215221  0.199928  0.179151  0.258760\n",
+      "RLDS                 0.543318  0.503186  0.194050  0.934344\n",
+      "\n",
+      "Fog-VLA-DM-lossless:\n",
+      "  On average, Fog-VLA-DM is 1.51x faster\n",
+      "  Median speedup: 1.53x\n",
+      "  Range: 1.28x to 1.71x faster\n",
+      "\n",
+      "H264:\n",
+      "  On average, Fog-VLA-DM is 1.37x faster\n",
+      "  Median speedup: 1.36x\n",
+      "  Range: 0.89x to 1.83x faster\n",
+      "\n",
+      "HDF5:\n",
+      "  On average, Fog-VLA-DM is 1.60x faster\n",
+      "  Median speedup: 1.51x\n",
+      "  Range: 1.36x to 1.89x faster\n",
+      "\n",
+      "LEROBOT:\n",
+      "  On average, Fog-VLA-DM is 0.22x faster\n",
+      "  Median speedup: 0.20x\n",
+      "  Range: 0.18x to 0.26x faster\n",
+      "\n",
+      "RLDS:\n",
+      "  On average, Fog-VLA-DM is 0.54x faster\n",
+      "  Median speedup: 0.50x\n",
+      "  Range: 0.19x to 0.93x faster\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df = pd.read_csv('./format_comparison_results.csv')\n",
+    "\n",
+    "# Update the format names\n",
+    "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n",
+    "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n",
+    "\n",
+    "# Calculate speedup factors\n",
+    "def calculate_speedup(group):\n",
+    "    fog_vla_dm_time = group[group['Format'] == 'Fog-VLA-DM']['AverageLoadingTime(s)'].values[0]\n",
+    "    group['SpeedupFactor'] = fog_vla_dm_time / group['AverageLoadingTime(s)']\n",
+    "    return group\n",
+    "\n",
+    "df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n",
+    "\n",
+    "# Get unique datasets\n",
+    "datasets = df['Dataset'].unique()\n",
+    "\n",
+    "# Create a plot for each dataset\n",
+    "for dataset in datasets:\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    sns.set_style(\"whitegrid\")\n",
+    "    \n",
+    "    # Filter data for the current dataset\n",
+    "    dataset_df = df[df['Dataset'] == dataset]\n",
+    "    \n",
+    "    # Create the box plot\n",
+    "    sns.boxplot(x='Format', y='SpeedupFactor', data=dataset_df[dataset_df['Format'] != 'Fog-VLA-DM'])\n",
+    "    \n",
+    "    # Customize the plot\n",
+    "    plt.title(f'Latency Speedup Factor of Fog-VLA-DM Compared to Alternatives - {dataset}')\n",
+    "    plt.xlabel('Format')\n",
+    "    plt.ylabel('Speedup Factor (higher is better)')\n",
+    "    plt.yscale('log')\n",
+    "    \n",
+    "    # Add a horizontal line at y=1 to represent Fog-VLA-DM\n",
+    "    plt.axhline(y=1, color='r', linestyle='--', label='Fog-VLA-DM')\n",
+    "    \n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Save the plot\n",
+    "    plt.savefig(f'latency_speedup_comparison_{dataset}.pdf')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Print summary statistics for the current dataset\n",
+    "    summary = dataset_df[dataset_df['Format'] != 'Fog-VLA-DM'].groupby('Format')['SpeedupFactor'].agg(['mean', 'median', 'min', 'max'])\n",
+    "    print(f\"\\nSummary for {dataset}:\")\n",
+    "    print(summary)\n",
+    "    \n",
+    "    # Print interpretation of the summary\n",
+    "    for format, stats in summary.iterrows():\n",
+    "        print(f\"\\n{format}:\")\n",
+    "        print(f\"  On average, Fog-VLA-DM is {stats['mean']:.2f}x faster\")\n",
+    "        print(f\"  Median speedup: {stats['median']:.2f}x\")\n",
+    "        print(f\"  Range: {stats['min']:.2f}x to {stats['max']:.2f}x faster\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e030fe63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lycsmk8rmOi8PGD1abkdEZCZ8l02e+a8WgIYNG9bavkGDBnYfa7Zq1SrMmDHDsnz16lUAwJ133ong4GAAwPDhwzFt2rQqjz116hROnTrlaOk2du/eXafHESlJSQlgnhNRkuy3kSRApZLbnTvH3TdEJBMeSPR6veW21oG/TCEhIZbbhYWFVdZfvnwZ2dnZVe7fuXOn5XZiYqLd5/7vf/9rc8RPfej1euTn57vkufyFXq+HJElQqVSiS6E6+v77QOTmhtbaTpKA3Fzgf/8rwr33lnmgMnI1fl6Vx9Xb1Pr73RHCA4mrTZo0qc7T1ruSJEmQqvsXkeyyfs/43vmmVasCoFZLlt0zNVGrJfz4YwDGjSv1QGXkavy8Ko+rt6mzzyE8kOh0OuTm5gIASkpKoNPpamxfXFxsuR0eHu7W2urDfE4fcpz5/eJ757tyc9UOhRFAHlOyenUghgzRoX17Ezp0MKFDByM6dDChbVsTgoLcXCzVCz+vyuPqberscwgPJFFRUZZAkpOTU2sguXLlis1jXWny5Mm45ZZb6vTY3bt3Y8qUKZZlnU6HyMhIV5XmN8zdhXzvfM/evcDJk849xmRSYceOAOzYYXu/RgPExwOJiUDHjvK1+eLijz3VAz+vyuPKbVrb93llwgNJQkICTpw4AQA4ceIEWrduXWN7c1vzY10pLi4OcXFxLn1OIqXbvh2YORNYutT5xzZqBOTkVB0AazQChw/Ll8pH4zRpYhtUzNctWsiDZYnINwkPJF26dLGcx2br1q0YPHhwtW0vXryI06dPAwBiY2PRqFEjj9RIRFVt3gzMmAGsXm17v0pV/RE21m2iogDzQW2HDwMHD8qXAwfk60OHAKs9tBYXLsiX9HTb+8PCbHtSzEGlXTvg+gF2ROTFhAeS22+/He+++y4AefbVF154odq2q63+8tU2qysRuUdGhtwj8ttvtvc3bQo8/7zcU3HvvfJ99oKJuRfjm28qDvnt2lW+WDOZ5MBSOagcPAhculT1ea9dA7Ztky/WNBqgbduqQSUxEYiOdv7nJyL3EB5IkpOT0aRJE1y4cAHp6enYvn273fPZGI1GfPzxx5bl8ePHe7JMIr8mSXIAmTEDyMy0XdeyJfDSS8DkyRUBIzhYnmckNxeWo27M11FRchgZObLm11Srgdat5cvtt9uuu3JF7kGxDikHDgAnTshBxprRCBw5Il8qzxDbuHHVkNKxoxyq1MKnjSTyL8IDiUajwfTp0/H4448DAB588EGsXbsWsbGxNu1eeukly1wiAwcOxG233ebpUon8jiQBq1bJPSKVp/dp2xZ4+WV5evjKR8SMGiVPerZ4MbBwYTlyc+XeiHHjAnHPPfWfDK1BA2DAAPliraQEOHq0alA5dAgoKqr6PBcvypeMDNv7Q0OBhISqQaV9e+7+IXKXOgeSEydO4D//+Y/Nfdazle7YsQNTp061WT9kyBAMGTKkynM9/PDDWLp0KX799Vfs27cP3bp1w8MPP4xOnTrh6tWrmD9/PrKysgDIR9Z88cUXdS2biBxgMsmDVGfOBKzmFAQgf1G/+ipw331AQA1/QbRa4P77gZEjizx2NIZWC3TuLF+smUzA6dP2d/9cvFj1eYqKgB07UOXoH7W6+t0/MTHu+7mI/EGdA8mff/6JWbNmVbt+9+7dVaZTDwgIsBtIAgIC8MMPP2DChAlYuXIlLly4YDP9u1mLFi2wYMEC3HDDDXUtm4hqYDQCCxcCs2YB+/bZruvSBZg6Fbj7bnlchi9Rq4FWreRL5c7Vq1flHpTKQeXYsaq7f0wmuQfm6FFg5UrbdbGx9oNKXBx3/xA5QvguG7Pw8HD8+OOPWL58Of73v/9h69atuHTpEsLDwxEfH4+77roLjz76KI93J3KDsjJg7lxg9mx5rIW1Hj2AadPk3TBK/GKNiQH695cv1gwG+7t/Dh60v/vn0iX5sn697f0hIXKvUuVDldu353l8iKzVOZCkpKS4Zbrg0aNHY/To0S5/XiKqymCQB5i++WbVSc3695eDyO23++f8HsHBwA03yBdrJhNw9qz9oHLhQtXnKS6Wd3tV3vWlUgFt2lQdp5KYKI+RIfI3XtNDQkSeU1wMfPUV8M47wJkztutSUuQgMniwfwaR2qjV8pFFLVsCQ4farsvLsz9O5dgxeXeYNUkCjh+XL6tW2a5r2NB+UGnVSpm9VEQAAwmRX9Hrgc8/B957r+pgzqFD5SAyaJCY2pQgKgro10++WDMY5FBSOagcPChvk8pycuTDqysfYq3VVt39k5gIdOgg7xoi8mUMJER+oKAA+OQTIC1NnsPD2siR8mDVPn3E1OYPgoOBTp3kizVJqn73z/nzVZ+npATYtUu+WFOp5Pla7PWqNGzo2p+lpARYtAhYtCgUV6/KY3DGjpUvHBND9cFAQqRgV68CH30EfPyxvDvB2t13y0Gke3cRlREgB4kWLeTLrbfarsvPt+1JMQeVo0ft7/45cUK+VJ7Kv0GD6nf/OHu01IoV1hPeBVgmvPvxR+Dppx2b8I6oOgwkRAp06RLwwQfAp58ChYUV96vVwPjxwCuvVB2sSd4lMhLo21e+WCstrX73j/W2NrtyBcjKki/WtFp5V0/loNKhgzwxXGUrVgBjxlQsm0wqm+u8PGD0aGDZMvmILCJnMZAQKcj588C778rjRKxPTBcQADzwgDyzavv24uqj+gsKksNDx47AnXdW3C9J8uy4lYPKgQPy/ZWVlAC7d8sXayqV3HtiHVLatJFn5DW/jj2SJD920iT59bj7hpzFQEKkAKdOyUfMfPWVPIDSLCgIeOgh+VwzrVsLK488QKUCmjeXLzffbLuuoMD+uX+OHgXKy23bSpJ8CPjJk8D1E7E7TJLk3TkffST3xIWGypeQEB4dRLVjIHFAWloa0tLSamxjsP4WAKDX65Gfn+/OshRHr9dbphgnx5w8qUJamhbz5weirKzifdNqJfzlL6V46ikDmjeX/6UV9evI7eodOnSQL9bTPJWVASdOqHH4sBpHjmhw6JAaR46ocfiwBoWFdd9eL70kX6xptRJCQqTrAUWCVguEhkoICcH1++Xb9u6zfpz9++Tr4GAeql4XJSXAsmWBWL48CLm5KkRHSxg9ughjxpTVq6dLb+8QshowkDigoKAAZ8+edeoxkiS5ZeI4JbN+z/je1ezwYTXS0rRYvDgQRmPFX+CwMAmTJxswZYoBjRub30tRVeL663O7equAAKB9eyPatzcCKLPcL0nAhQsqS0j54AMtzp+vXxdHSYkKJSUq5ObWs+gaqFQV4aQi3Ni/rdXaCzhVw46924GB7vsZPG316gA8/ngo8vPVNmfmXrNGhRdfNOFf/yrCHXeU1/5Edjj7eWcgcUBERASaN29eYxuDwYCcnBzLskql4n+ETjK/X3zvqrdvnxrvvx+MpUsDIUkV71FEhIRHHjHgscdK0aCB+Y+Ad7yH3K6+R6UCmjUDmjUzIjnZiMzMQKxapbIMYK2ZhJYtJfTqVY7iYtX1C1BUJF8XF6tQVCRfGwyu/X2QJBWuXQOuXXPv71lAQHWBxbkenprDj/vPGbV6dQDuv79iBHPlgcoFBSpMnBiGuXOLMGyY86HE2c+7SuK/LC6RlZWFpKQky3JmZiYGcYYpp+Tn53vsrLC+Zts2+cy7y5bZ3h8dDTzzDPDkk/KkXN6I29X3ffttxaBWR9vff3/t7YxGXA8r9b9U9zzXrlU9TNpXBAdXjMNx9UWtlo+0y8+vuRdVpZL/ttRloLKz34vsISHyYps2yUGk8twSjRoBzz4LPP44EB4upjbyH2PHyvOM5OU59uV1zz2OPa9GA+h08sWdyspcE3pqCz+u/vfeYJAv7tzNVRvzQOXFix0LmfXBQELkhTIygBkzgN9/t72/aVPghReARx6xP1cEkTtotfKkZ6NHy6HD3hevuXf+m2+875DfwEB5Xhd3dtBJkhweXBl87IWfkhL3/QzVUauBpUsZSIj8hiQBv/4q94hUPodJXJx81MJDD3nfH3vyDyNHyrsMK2ZqrRgAaTKpEBXl3zO1qlTyZ1OrlafTdxejUQ4l9Q06a9fKMzk7wmRyvG19MJAQCSZJ8tleZ8wAtmyxXde2rTyr6gMPyHOKEIk0apQ8lmDxYmDhwnLk5srjmMaNC8Q99zAse4JGA4SFyZf6uPtuOWCaTLW3VavdG7LMGEiIBDGZ5G7QmTOBnTtt1yUmAq++Kk8uFcBPKXkRrVbuuh85soiDlX3YmDHAkiWOtTWZbGcFdhfOnUfkYUYjMG8e0KWLPPjPOox07QosXAjs3Sv/0WcYISJ3GDtW7t2q7chclUpu5+hA5fpgICHykLIyYM4c+fwgEycC+/dXrOvZU+4+3bFD/kPh7vkHiMi/mQcqA9WHEk8PVGYgIXIzgwH44gt52u6HHgKOHKlYN2AAsGYNsHWrfAQDz/dBRJ5iHqhsnsNIBZPNdVQUsHy55wYqs0OYyE2Ki+WT3b3zDnDmjO26lBRg2jRg8GCee4OIxBk1Cvj+ifX4fEYODiIRxQhBCIqRiIP42xMNMXTkTR6rhYGEyMX0euDzz4H33gMuXrRdd9ttwNSpACfxJSJvkDEjA5tmpKMbgG7YY7Nu0wwgOFBC8rRkj9TCQELkIvn5wCefAB98AFy5Yrtu1Cj5qJk+fcTURkRUWcaMDKRPT6+xjXm9J0IJAwlRPV29Cnz0EfDxx/LU2mYqlXys/6uvAt27i6qOiKgqR8KImadCCQMJUR1dugSkpQGffirvpjFTq4H77pMnNOvUSVx9RET2OBNGzDwRShhIiJx07pw8PuTzz+WBq2YBAfIZUV96CWjfXlx9RETVqUsYMXN3KOFBhkQOOnUKmDJFns79gw8qwkhQEPC3v8mH8/7nPwwjROS90l9LF/r4mrCHhKgWx44Bb74pTw5UXl5xv1YLPPoo8PzzQPPm4uojIqpNqb4UR386itgusbi0+1Kdnyfl9RSX1VQZAwlRNQ4eBGbPlqd5Nxor7g8LAx5/HHj2WaBxY3H1ERHVRH9Rj8M/HsbBZQdx/LfjMBqMtT+oBilvpHAMCZEn7dkjn/Bu0SL5TLxmERHAU08Bf/870KCBsPKIiKp19ehVHFx2EAeXHcTpjacBqWqb4IhgRLaKxKU9jveUuDuMAAwkDklLS0NaWlqNbQwGg82yXq9Hfn6+O8tSHL1ebzl7qAg7d6rx7rtarFoVaHN/dLQJjz9eiocfNlimWOamdZzo7Uruwe3qHSRJwsUdF3F89XEcW3UMVw5csdsurGkY4ofHI354PFoMbAFNkAbZ72Rj0+xNtb5G/1f6o/tT3Z3+TtNbH37oAAYSBxQUFODs2bNOPUaSJEiSnWhK1bJ+zzz53m3ZosG772rx22+2QaRRIxOmTDFg8mQDwsPNNXqsLMUQtV3JvbhdxTGWGnFmwxkcX3Ucx9cch/6c/S/+mMQYxA+LR9vhbdG4e2Oo1BXhUZIk9Hm+DyRI2Dx7c7Wv1e+VfnK7OmxjZx/DQOKAiIgINK9l1KLBYEBOTo5lWaVS8T8HJ5nfL0+8d5IEZGXJQWT9etuPQdOmJjz1lAF/+UspQkMBgNuxPjy5XclzuF09q7SwFCd/P4ljq47hxM8nUFpQWrWRCmjap6ncEzIsHtHtomt93n4v9IMKKrs9Jf1f6Y++L/Stc83O/l4wkDggNTUVqampNbbJyspCUlKSZVmn0yEyMtLdpSmOuQvYXe+dJAG//grMmAFkZdmua9VKnkNk0iQ1tNoQACFuqcEfuXu7khjcru6lv6DHoR8P4dCyQ/Kg1NKqg1I1QRq0vbUtEkYnIGFkAnRNdE6/ztBZQxGsDbaZn8QVY0Z0OudqYSAhvyBJwMqV8mDVLVts18XHy7OqPvAAEBho//FERJ5w5fAVy6DUM5vP2B+UGhmMDiM6IHFMIuJvi0dweHC9Xzd5WjIMJQZsenMT+r/c32Mn1LPGQEKKZjIBS5bIQWTXLtt1HTvK55m59155llUiIk+TTBLO/XHOEkJyDuTYbRfePByJYxKROCYRrZJbQROocXktfV/oiz7P9xG2C45/hkmRysuBhQuBWbOA/ftt13XtCkydKp/4Ts25ionIw4ylRpxMP4mDyw7i0PJDKDxXaLddoxsaWUJI055NFT9Wh4GEFKWsDPjuO3lCs6NHbdf16gVMmwaMGMEgQkSeZSgw4MiaIzi07BCOrD4CQ4GhaiMVEDcwDgljEpA4OhEx7WI8X6hADCSkCAYDMGcO8NZbwMmTtusGDJCDyG23AQr/B4OIvEjh+UIcWnF9UOrvx2EqM1VpownWIP7WeCSMSUCHER2ga+z8oFSlYCAhn1ZcDHz5JfDOO0DlqWIGD5aDSEoKgwgReUbOoRx5V8yyQ/KgVDu0UVp0GNEBCWMS0O62dgjSBXm4Su/EQEI+Sa8H/vUv4P33gYsXbdfdfrs8RmTgQDG1EZH/kEwSzm45axkPknPQ/qDUiJYRlvEgcUlxbhmU6usYSMin5OcDn3wCfPABcKXSDMmjR8tHzfTuLaY2IvIP5YZynFxXMShVf8H+TKmxXWItIaTJjU0UPyi1vhhISLiSEvlEdosWheLqVSAmBhg7Vr5otXKbq1eBDz8EPv7Y9jwyKhVwzz1yEOnWTUj5ROQHSvJLcHTNURxcdhBHVh9BaWHVmVJVahVaDmyJxDGJSBidgJh4/xqUWl8MJCTUihXApElAbi6gVgfAZFJBrZbw44/A008DH30E7NsHfPqpvJvGTK0GJkyQJzTr2FFY+USkYIXn5EGpB5cdxIm1J+wOSg3QBiB+aMWg1LBGYQIqVQYGEhJmxQpgzJiKZZNJZXOdmws8+KDtYwIC5Ptefhlo185DhRKRX5AkCTkHKwalnt1i/6Sq2mgtEkYmIGFMAuKHxiMojINSXYGBhIQoKZF7RgDHzqAbGAj89a/Aiy/K55whInIFySThTPYZSwi5cviK3XaRcZHy/CBjEtEqqRXUAZzMyNUYSEiIRYvkHhBHvfce8NRT7quHiPxHeUk5Tqw9IYeQFYdw7eI1u+0ad2tsGQ/SpDsHpbobAwm5nSQB58/LU7jv3w8cOAAsXuz449VqICODgYSI6q4krwRHVh/BwWUHcXTNUZTq7Q9KjUuKs4SQ6DbRAir1Xwwk5DImE3D6tG3wMN+2PjKmLs979arr6iQi/1BwpsAyKPXkupMwldsZlBoiD0pNHJOIDiM6ILRhqIBKCWAgoTowGoETJ6oGjwMHgGv2ez7rRa2WDwUmIqqJJEnIOZBjOXPuua3n7LYLaRBSMSj11ngEhgZ6uFKyh4GEqlVWJp+grnLwOHhQPneMo1q1kg/N7dSp4rJrF/DYY4493mQC7ryzbj8DESmbyWjCmc0Vg1KvHrXfnRrVOsoyKDVuYBwHpXohBhIvZZ4sbNkyeUbSBg3kQ2StJwtz5WsdPlw1eBw+DJSXO/YcKhUQH181eCQmAjo754q68UZ5DpG8vJqPslGpgKgoefIzIiJAHpR6/PfjOLjsIA6vOIxrl+x3zTbp3sQSQhp3bcxBqV6OgcQL2U4WJvcQqNXAkiXyZGHffAOMHOn88167JvduVA4ex47Jr+GIgACgffuqwaNDByAkxPFatFr55xg9Wg4d9kKJ+W/HN9+4PoQRkW8pzi3GkdVHcGjZIRxZcwRl18qqtFGpVWh1Uys5hIxORFTrKM8XSnXGQOJlqk4WZnudlyd/iS9bBowaZf858vNtB5Sab5886XgdQUFy70bl4NGunbzOFUaOlH+OivAlWWZqNZlUiIqqe/giIt+Xfzofh5bLg1L/zPiz2kGp7W5vh8QxiWg/vD1CG3BQqq9iIPEijkwWJklyz8GkScCePcDx4xXBwxw+ztqfXNCu0FA5eFiHjk6dgDZt5N4Qdxs1Cjh3Tj4MeOHCcuTmAtHRwLhxgbjnHvaMEPkTSZJwed9ly6DU89vO220X0iAECaPkXTFtb2nLQakKwUDigLS0NKSlpdXYxlBplKder0e+k8e6fv99IHJza0/3kiT3KLRo4fhzh4dLSEgwIiHBhMRE+TohwYiWLSWo7YztcsfRMjUZORIYPFgPSZKgUqmg0+lgMDg3eJa8k15fsV1JOTJnZmLb+9vQ89meSJqaVOfnMRlNOL/lPI6tOoZjq44h/4T9v5sRrSIQPzwe8cPj0axvM8ug1KKyIqAe0wpQBVd/VvV6+2dBrg4DiQMKCgpw1pluB8hJX3JkTnQrq1YFWHZX1FV0tMkSNqyDR9OmEqr7HXOyTLexfs+cfe/Ie3G7Kk/2u9nY9t42AMC297YhMCgQfZ/v6/Djy4vLcSr9FI6tPoYTP51AcU6x3Xax3WLRdnhbxA+LR4NODWy+KPm75Hqu/qw6+xwMJA6IiIhA8+bNa2xjMBiQk5NjWVapVE6nzNxctVNhJDLShHHjyqwCiAkNG1YXPLz/v1Pz+1WX9468F7ersmS/k43Nszfb3Ld59maooELfF6oPJSW5JTjx8wkcW3UMJ38/ifKiqofwqTQqtBjYAvHD49H2jraIiItwef1UPVd/Vp19DgYSB6SmpiI1NbXGNllZWUhKqui21Ol0iIyMdOp1YmMrjqqpjVoN3HyzGv/+d7BTr+HtzN2Fzr535N24XZUhY0YGNs3eZHfdptmbEKwNRvK0ZMt9+afycXC5PD/IyYyTkIxV/2MODA1Eu9vbIWFMAjoM74CQGCcO1yOXc+VnVWdvzocaMJB4kTFj5EN7HcHJwojIkzJmZCB9enqNbdKnp+PaxWsIaxyGQ8sO4fx2+4NSQxuFImFUAhJGJ8iDUkM4KJUYSLzK2LHyPCOcLIyIvIkjYcRs66db7d4f3TYaiXcmInFMIlr0bwG1hjOlki0GEi/CycKIyNs4E0Yqa9qzKRLHyCGk0Q2NOIaIasSI6mXMk4VFRcnL5kNyzddRUcDy5ZwsjIjcrz5hBAASRifgpqk3IbZzLMMI1Yo9JF7IerKwpUuBq1fls93eeSc4WRgReUz6a+n1frz1IFeimjCQeCmtFrj/fvlCRCTCoJcGIevNrDo/PuX1FJfVQsrHQEJERDauXbqGTWmbsPUz+wNUHZHyRgp7R8gpDCRERAQAKDhTgA3vbsD2L7ejvNhq4jIVACcm3WQYobpgICEi8nNXj13Fhrc3YOecnTCVVczMqAnSoPvk7hj4wkDs/m63QwNcGUaorhhIiIj81OX9l5H1Zhb2zNsDyVTRBRIYGoiej/bEgOcGILxZOABYQkZNoYRhhOqDgYSIyM+c33EembMycWDJAZtdMcERwejzZB/0fbovwhqFVXlcTaGEYYTqi4GEiMhPnN54GutnrsfRNUdt7g9pEIJ+z/RDnyl9oI2qeV4Be6GEYYRcgYGEiEjBJEnCibUnkDkzEyfTT9qs0zXVYcBzA9Dz0Z4ICgty+DmTpyXDUGLApjc3of/L/RlGyCUYSIiIFEiSJBxeeRiZszJxNvuszbrIVpEY9NIgdJ/UHQHaun0N9H2hL/o834czsJLLMJAQESmIyWjCgR8OIHNWJi7uvmizrkGHBhj0yiB0mdAFmkCNoAqJ7GMgISJSAGOZEXvm7UHWm1m4cuiKzbrGXRsj6dUkdLy7I8+yS16LgYSIyIeVl5Rj55yd2PD2BuSdzLNZ17xvcyS9moQOIzpw1wp5PQYSIiIfVHqtFNu+2IaN722E/rzeZl3rlNZIejUJbW5uwyBCPoOBhIjIh5Tkl2DLJ1uQ/WE2inKKbNa1u6Mdkl5NQtzAOEHVEdUdAwkRkQ8oyinC5g83Y8snW2DIN9is63hXRwx6ZRCa9WwmqDqi+mMgISLyYoXnCrHx/Y3Y9vk2lBWVWe5XqVXofF9nDHp5EGJviBVYIZFrMJAQEXmhvJN52PDOBuz4zw4YS42W+9WBanT7SzcMenEQYtrFCKyQyLUYSIiIvEjOoRxseGsDdn+3G6byijPvBmgD0OORHhjw3ABEtowUWCGRezCQEBF5gQu7LiBrdhb2Ldpnc8K7IF0Qek/pjX7P9IOusU5cgURuxkBCRCTQmewzyJyVicM/Hra5XxutRd+n+6Lvk30REhMiqDoiz2EgcUBaWhrS0tJqbGMw2I561+v1yM/Pd2dZiqPX6yFJEudNUBhu16okScKZrDPY+v5WnEo/ZbMutFEoejzRA10nd0VQeBBKUYrS/FJBlVaP21V5XL1N9Xp97Y2sMJA4oKCgAGfPnq29oRVJkiBJUu0NycL6PeN7pxzcrhUkScKfv/2JLe9twfkt523W6Zrp0PPpnuj8QGcEhARY2nsrblflcfU2dfY5GEgcEBERgebNm9fYxmAwICcnx7KsUqn4n4OTzO8X3ztl4XYFJJOEYyuPYcv7W3Bp1yWbdZFtItH7md7oOL4jNEG+c8I7blflcfU2dfY5GEgckJqaitTU1BrbZGVlISkpybKs0+kQGcmR8M4ydxfyvVMWf92upnIT9i7Yi6zZWbi8/7LNukY3NELSK0m4YdwNUAf45gnv/HW7Kpkrt6lO59wgbAYSIiIXKzeUY9f/dmHDWxuQezzXZl3Tnk2R9GoSEkcnQqVmzwKRGQMJEZGLlBWVYftX27Hx3Y0oOFNgsy5uUBySXk1C/G3x3MVBZAcDCRFRPRkKDNj6r63YnLYZ1y5ds1nX9ta2uGnqTWh1UytB1RH5BgYSIqI6Kr5ajOyPs5H9cTZKckts1iWMSkDSq0lo3qfmAfFEJGMgISJykv6CHpvSNuGPf/2BUr3VHCEq4IZxNyDplSQ07tpYXIFEPoiBhIjIQfmn8rHh3Q3Y8dUOlJeUW+5XB6jR9YGuGPTSIDTo0EBghUS+i4GEiKgWV49eRdZbWdj1v10wlVWc8E4TrMGN/3cjBr4wEFGtosQVSKQADCRERNW4tO8SsmZnYe/3eyGZKmadDAwNRK/HeqH/s/0R3jRcYIVEysFAQkRUyblt55A5KxMHlx60uT84Mhh9nuyDfk/3Q2jDUEHVESkTAwkR0XWnsk5h/cz1OPbzMZv7QxuGot8z/dB7Sm9oI7WCqiNSNgYSIvJrkiTh+G/HkTkzE3+u/9NmXXizcPR/rj96PtITQWFBgiok8g8MJETklySThMMrD2P9zPU4t/Wczbqo1lEY+NJAdJ/UHQHB/DNJ5An8pBGRXzEZTdi/aD8yZ2fi0h7bM+82TGyIQS8PQuf7OkMT6Dtn3iVSAgYSIvILxjIjdn+3Gxve2oArh6/YrGvcrTGSXk1Cx7s6Qq3xzTPvEvk6BhIiUrTyknLs+O8ObHhnA/L/zLdZ16JfCyRNTUL7Ye15wjsiwRhIiEiRSvWl+OPzP7Dp/U3QX9DbrGs9uDVumnoTWg9uzSBC5CUYSIhIUUrySpD9z2xkf5iN4qvFNuvaD2uPpFeT0HJAS0HVEVF1GEiISBGuXb6GzR9sxtZPt8JQYKhYoQI63d0Jg14ZhKY3NhVXIBHViIGEiHxawdkCbHxvI7Z9sQ3lxRUnvFNpVOgyoQsGvTwIjTo2ElghETmCgYSIfFLuiVxseHsDdn69E8ZSo+V+daAa3R/qjkEvDkJ022iBFRKRMxhIiMin5BzMQdabWdg9dzckY8UJ7wJCAtDzkZ4Y8NwARLSIEFghEdUFAwkR+YQLOy8gc3Ym9i/eD1TkEASFB6H3lN7o/0x/hMWGiSuQiOqFgYSIvNrpTaeROSsTR1Ydsbk/JCYEfZ/uiz5P9kFIdIig6ojIVRhIiMjrSJKEk+knkTkzEyfWnrBZF9Y4DAOeG4Cej/ZEcHiwoAqJyNUYSIjIrbLfycamNzeh/8v9MXTW0BrbSpKEI6uPIHNWJs5sOmOzLqJlBAa+OBA3Tr4RgSGB7iyZiARgICEit8mYkYFNszcBADbN3oRgbTCSpyVXaSeZJBxYcgCZszJxYecFm3Ux7WIw6OVB6Hp/V2iCeMI7IqViICEit8iYkYH06ek295mXzaHEVG7Cnvl7kDU7CzkHc2zaxnaOxaBXBuGGsTdAHcAT3hEpHQMJEbmcvTBilj49HaZyE8KbhWPD2xuQdyLPZn2zXs2Q9GoSEkYlQKXmeWaI/AUDCRG5VE1hxGz9G+ur3BeXFIebpt6Etre25QnviPwQAwkRuYwjYaSy+NvikfRqEloltXJPUUTkExhIHJCWloa0tLQa2xgMBptlvV6P/Px8d5alOHq9HpIk8b9jH5X9TrZlAKszYnvGIqprFD8vPoafV+Vx9TbV6/VOtWcgcUBBQQHOnj3r1GMkSYIkSbU3JAvr94zvnW/Jfjcbm2dvrtNjN83eBAkS+j7f18VVkTvx86o8rt6mzj4HA4kDIiIi0Lx58xrbGAwG5ORUHCWgUqn4n4OTzO8X3zvfs/nNuoUR68f3e6Gfi6ohT+DnVXlcvU2dfQ4GEgekpqYiNTW1xjZZWVlISkqyLOt0OkRGRrq7NMUxdxfyvfMtKa+nOD12pPLjuc19Dz+vyuPKbarT6Zxqz4P7iajekqclI+WNlDo9NuWNFLuTpRGRf2EgISKXSJ6WjM4TOjv1GIYRIjJjICEilzi75SwOLj3ocHuGESKyxkBCRPV29dhVzBsxD+XF5QCA2C6xNbZnGCGiyhhIiKheinKKMPeOuSi6XAQAaJ3SGg9vfbjaMSUMI0RkDwMJEdVZWXEZvh/9Pa4euQoAaNSpEcYtGYeA4AC7A10ZRoioOgwkRFQnkknC0geW4vTG0wAAXRMdJqyegJDoEEub5GnJ6P9Kf0AF9H+lP8MIEVWL85AQUZ388twvOPDDAQBAYFggJqyegKhWUVXa9X2hL/o834eTZxFRjdhDQkRO2/zRZmz+QJ6dVaVRYdzicWh6Y1PBVRGRL2MgISKnHFhyAD8/87NlecTnI9Du9nYCKyIiJWAgISKHnd54GksmLgGunzMraWoSevy1h9iiiEgRGEiIyCFXjlzB/FHzUV4izzXS9YGuGPzGYMFVEZFSMJAQUa2uXbqGuXfMRfGVYgBAm5vbYNRXozhQlYhchoGEiGpUVlSG+aPmI/dYLgAgtnMsxv0wDpogjeDKiEhJGEiIqFomowk/TPgBZ7PPAgDCm4djwuoJ0EZqBVdGRErDQEJEdkmShJ/+/hMOLT8EAAgKD8LE1RMR2TJScGVEpEQMJERk16a0Tdj6yVYAgDpAjXE/jEPjro0FV0VESsVAQkRV7Fu0D78+96tleeSXIxF/a7zAiohI6RhIiMjGqaxTWPrAUstyyusp6D6pu7B6iMg/MJAQkUXOoRzMHzUfRoMRANB9cnfcNO0mwVURkT9gICEiAID+oh5z75iLktwSAED80HiM+HwE5xohIo9gICEilF4rxfwR85F3Ig8A0LhbY4xdNBaaQM41QkSewUBC5OdM5Sb8MP4HnPvjHAAgomUEJq6eiOCIYMGVEZE/YSAh8mOSJGH1k6txeOVhAEBwZDAmrp6I8GbhgisjIn/DQELkxza8swHbPt8GAFAHqnHv0nsR2zlWcFVE5I8YSIj81J55e/D7S79blkd/PRptBrcRWBER+TMGEiI/dDLjJJY/tNyyPGTWEHSd2FVgRUTk7xhIiPzM5f2XsWDMAhhL5blGejzSA4NeHiS4KiLydwwkRH6k8HyhPNdInjzXSPth7TH80+Gca4SIhGMgIfIThkID5g2fh/xT+QCApj2a4p4F90AdwD8DRCQe/xIR+QFTuQmLxy3GhR0XAACRrSIxYdUEBOmCBFdGRCRjICFSOEmSsPKxlTj601EAgDZKi4lrJkLXRCe4MiKiCgwkRAqXOSsTO77aAQDQBGkwfvl4NOrYSHBVRES2GEiIFGzX/3Zh3bR1luUx34xBq5taCayIiMg+BhIihTr++3Gs+L8VluVb3r4Fncd3FlgREVH1GEiIFOjinotYeNdCmMpNAIBej/fCgOcHCK6KiKh6DCREClNwpgDzhs2DocAAAOgwsgPu+PgOzjVCRF6NgYRIQQwF8lwjBWcKAADNejfD3fPvhlrDjzoReTf+lSJSCGOZEQvvWYiLuy8CAKLaRGHCygkICuNcI0Tk/QJEF+AL0tLSkJaWVmMbg8Fgs6zX65Gfn+/OshRHr9dDkiTuWqgDSZLw65RfcfzX4wAAbbQWoxaOQnlwufDfQ25XZeJ2VR5Xb1O9Xu9UewYSBxQUFODs2bNOPUaSJEiS5KaKlMn6PeN755zNb23G/nn7AQCaYA1GzhuJ6HbRXvE+crsqE7er8rh6mzr7HAwkDoiIiEDz5s1rbGMwGJCTk2NZVqlU/M/BSeb3i++dc/Z9tw/Zb2fLCyrg9n/fjub9a/599SRuV2XidlUeV29TZ5+DgcQBqampSE1NrbFNVlYWkpKSLMs6nQ6RkZHuLk1xzN2FfO8cc+yXY/j9779bloe+PxS9HuwlsCL7uF2VidtVeVy5TXU6505PwUGtRD7qws4LWHh3xVwjfZ7qg35/7ye4KiKiumEgIfJB+afyMXfYXJTqSwEAiXcm4ra029h1TkQ+i4GEyMeU5JVg7rC50J+XR7C36NcCd829i3ONEJFP418wIh9iLDViwV0LcHnfZQBATLsYjF8xHoEhgYIrIyKqHwYSIh8hSRJW/N8KnFx3EgAQ2jAUE9dMRFijMLGFERG5AAMJkY9YN20ddn+3GwAQEBKA+368DzHtYgRXRUTkGgwkRD5g27+3IXNWprygAu6edzda9GshtigiIhdiICHyckdWH8Gqx1dZlm//6HYkjkkUWBERkesxkBB5sXPbzmHRuEWQjPIUzP2f7Y++T/YVXBURkesxkBB5qbyTeZg/Yj7KrpUBADqN7YRb37lVcFVERO7BQELkhYpzi+W5Ri7Ic420HNgSd/7vTqjUnPiMiJSJgYTIy5QbyrFgzALkHJBP1tggoQHGLx+PAC1PPUVEysVAQuRFJJOE5ZOW48/1fwIAwmLDMHHNRIQ2CBVcGRGRezGQEHmR317+DXu/3wsACAwNxIRVExDdJlpwVURE7sdAQuQltn62FRvf2QgAUKlVuGfBPWjWq5ngqoiIPIOBhMgLHFpxCGueXGNZHvbpMHQY0UFgRUREnsVAQiTY2a1nsXj8Ykgmea6RgS8ORK+/9RJcFRGRZzGQEAmUezwX80fMR3lxOQCg832dcfPsmwVXRUTkeQwkRIIUXSnC3Dvm4tqlawCAVsmtMPrr0ZxrhIj8EgMJkQBlxWX4fvT3uHL4CgCgYceGuHfpvQgI5lwjROSfGEiIPEwySVj24DKc3nAaAKBrosPENRMREh0iuDIiInEYSIg87Jfnf8H+xfsBAIFh8lwjUa2ixBZFRCQYAwmRB2V/nI3NaZsBACqNCmMXjUXTHk0FV0VEJB4DCZGHHFh6AD/9/SfL8ojPR6D9He0FVkRE5D0YSIg84PSm01gyYQkgTzWCpKlJ6PHXHmKLIiLyIgwkRG525cgVzB85H+Ul8lwjXR/oisFvDBZcFRGRd2EgIXKja5evYe4dc1F8pRgA0GZIG4z6ahRUKs41QkRkjYGEyE3Kisowf+R85B7LBQDEdo7FuCXjoAnSCK6MiMj7MJAQuYHJaMKSiUtwNvssACC8WTgmrJ4AbaRWcGVERN6JgYTIxSRJws/P/IyDyw4CAILCgzBh9QREtowUXBkRkfdiICFysc0fbMaWf24BAKgD1Bi3eByadGsiuCoiIu/GQELkQvsW7cMvz/5iWR755UjED40XWBERkW9gICFykVNZp7D0gaWW5eR/JKP7pO7iCiIi8iEMJEQukHMoB9+P/h5GgxEA0P2h7kieniy4KiIi38FAQlRP+ot6ea6Rq/JcI/FD4zHiixGca4SIyAkMJET1UHqtFPNHzEfeiTwAQONujTF20VhoAjnXCBGRMxhIiOrIVG7CD+N/wLk/zgEAIlpEYMKqCQiOCBZcGRGR72EgIaoDSZKw5qk1OLzyMAAgOCIYE9dMRETzCMGVERH5JgYSojrY+O5G/PGvPwAA6kA17l16L2I7xwquiojIdzGQEDlpz/w9+O3F3yzLo/87Gm2GtBFYERGR72MgIXLCyYyTWD5puWV58MzB6Hp/V4EVEREpAwMJkYMu77+MBWMWwFgqzzXS4+EeSHolSXBVRETKwEBC5IDC84WYe8dclOSVAADaD2uP4Z8N51wjREQuwkBCVItSfSnmDZ+H/FP5AICmPZringX3QB3Ajw8RkavwLypRDUzlJiwatwgXdlwAAES2isSEVRMQpAsSXBkRkbIwkBBVQ5IkrHxsJY6uOQoA0EZpMXHNROia6ARXRkSkPAwkRNXInJ2JHV/tAABogjQYv3w8GnVsJLgqIiJlYiAhsmP3d7uxbuo6y/KYb8ag1U2tBFZERKRsDCRElZxYewLLJ1fMNXLL27eg8/jOAisiIlI+BhIiK5f2XsKCOxfAVGYCAPR6rBcGPD9AcFVERMoXILoAX5CWloa0tLQa2xgMBptlvV6P/Px8d5alOHq9HpIkCZvbQ39Oj+9v/x6GAnlbtr29LQbMGICCggIh9SiF6O1K7sHtqjyu3qZ6vd6p9gwkDigoKMDZs2edeowkSZAkyU0VKZP1e+bp985QYMCyccugPyt/gBr3aIzbv7odKo2K27GeRG5Xch9uV+Vx9TZ19jkYSBwQERGB5s2b19jGYDAgJyfHsqxSqfifg5PM75en3ztjmRGrJ61Gzl55+0W0isCo70dxrhEXEbVdyb24XZXH1dvU2edgIHFAamoqUlNTa2yTlZWFpKSK85rodDpERka6uzTFMXcXeuq9kyQJKyavwKl1pwAAITEheODnB9CwfUOPvL6/8PR2Jc/gdlUeV25Tnc65OZs4qJX8WsYbGdg5ZycAQBOswfgV49EwgWGEiMjTGEjIb+34egcy/pEhL6iAu767C3ED48QWRUTkpxhIyC8d++UYVj6y0rI89L2h6HRPJ4EVERH5NwYS8jsXdl3AwnsWwlQuzzXS56k+6PdMP8FVERH5NwYS8iv5p/Mxb9g8lBaWAgAS70zEbWm38SgBIiLBGEjIb5TklWDesHkoPFcIAGjRrwXu+u4uqDX8GBARica/xOQXjKVGLLx7IS7tvQQAiGkXg/ErxiMwNFBwZUREBDCQkB+QJAkr/roCJ9aeAACENgzFxDUTEdYoTHBlRERkxkBCirdu+jrs/nY3ACBAG4D7frwPMe1iBFdFRETWGEhI0bZ9uQ2ZMzPlBRVw17y70KJfC7FFERFRFQwkpFhH1hzBqsdWWZZv/+h2dLyzo8CKiIioOgwkpEjnt5/HorGLIBnls032S+2Hvk/2FVwVERFVh4GEFCfvZB7mDZ+HsmtlAIBO93TC0HeHCq6KiIhqwkBCilKcW4y5w+ZCf0EPAGg5sCXu/PZOqNSc+IyIyJsxkJBilBvKseDOBcg5kAMAaNChAcYvH48AbYDgyoiIqDYMJKQIkknC8knL8WfGnwCAsNgwTFwzEaENQgVXRkREjmAgIUX4/ZXfsff7vQCAgJAA3LfyPkS3jRZcFREROYqBhHze1n9txYa3NwAAVGoV7llwD5r3bi64KiIicgYDCfm0Qz8ewpon1liW7/jkDiSMTBBYERER1QUDCfmss1vP4ofxP0AyyXONDHxxIHo/1ltwVUREVBcMJOSTco/nYv6I+Sgrkuca6Ty+M26efbPgqoiIqK4YSMjnFF0pwtw75uLapWsAgFbJrTB6zmjONUJE5MMYSMinlJeU4/vR3+PK4SsAgIYdG+LepfciIJhzjRAR+TIGEvIZkknC0geX4vSG0wAAXRMdJq6ZiJDoEMGVERFRfTGQkM/49YVfsX/RfgBAYFggJqyagKhWUWKLIiIil2AgIZ+Q/c9sbHp/EwBApVFh7KKxaNqjqeCqiIjIVRhIyOsdXHYQPz39k2V5+L+Go/0d7QVWRERErsZAQl4j+51sfBTzEbLfybbcd2bzGfxw3w+APNUIkl5NQs+HewqqkIiI3IWHJpBXyJiRgU2z5V0ym2ZvQrA2GF3u64L5I+ejvKQcAND1ga4YPGOwyDKJiMhNGEhIuIwZGUifnm5zX/r0dGR/mI3iq8UAgDZD2mDUV6OgUnGuESIiJeIuGxLKXhgxM4eR2M6xGLdkHDRBGg9WRkREnsRAQsLUFEasxd8WD22k1v0FERGRMAwkJISjYQQANr2/CRkzMtxbEBERCcVAQh7nTBgxS5+ezlBCRKRgDCTkUXUJI2YMJUREysVAQh6V/lq60McTEZF3YiAhj0p5PUXo44mIyDsxkJBHJU9LRsobKXV6bMobKUieluzagoiIyCswkJDH1SWUMIwQESkbAwkJkTwtGUlTkxxqyzBCRKR8DCQkTGBoYK1tGEaIiPwDAwkJUZxbjI3vbJQXqjk9DcMIEZH/YCAhITa+txEleSUAgO4Pda8ypoRhhIjIv/Bsv+Rx+ot6ZH+YDQDQBGmQPD0ZUa2iYCgxYNObm9D/5f4MI0REfoaBhDwuc3YmyorKAAA9/9YTUa2iAAB9X+iLPs/3gUpVzT4cIiJSLO6yIY/K+zMP2z7fBkAe1Jr0imNH2hARkbIxkJBHZbyeAWOpEQDQ9+99oWusE1wRERF5AwYS8picgznY9c0uAIA2SouBzw8UXBEREXkLBhLymHXT10EySQCAgS8OhDZKK7giIiLyFgwk5BHnt5/H/kX7AQBhjcPQ58k+gisiIiJvwkBCHrF26lrL7Zum3oSgsCCB1RARkbdhICG3+zPzTxxdcxQAENkqEj0e7iG4IiIi8jYMJORWkiRh7SsVvSMp/0hBQDCnvyEiIlsMJORWR386ilNZpwAADRMbouv9XQVXRERE3oiBhNxGMklY+2pF78jgmYOhDuCvHBERVcVvB3Kb/T/sx4UdFwAATXs2Rce7OgquiIiIvBUDCbmFqdyEddPWWZaHzBrCc9QQEVG1GEjILXZ9uwtXDl0BALS6qRXih8YLroiIiLwZAwm5XLmhHBn/yLAss3eEiIhqw0BCLrfti23IP5UPAGg/rD3iBsUJroiIiLwdAwm5VKm+FJmzMi3Lg2cOFlgNERH5CgYScqnsj7Nx7dI1AMAN996Apjc2FVwRERH5AgYScpni3GJseGcDAEClUWHwG+wdISIixzCQkMtsfHcjDPkGAED3Sd3RoEMDwRUREZGvYCAhl9Bf0CP7o2wAgCZIg+TpyYIrIiIiX8JAQi6xftZ6lBWVAQB6PdYLkXGRgisiIiJfwkBC9ZZ3Mg/bvtgGAAgMC0TSK0mCKyIiIl/DQEL1lvF6BkxlJgBAv2f6ISw2THBFRETkaxhIqF4uH7iMXf/bBQDQRmsx4NkBgisiIiJfxEBC9ZI+PR2SSQIADHxxILRRWsEVERGRL2IgoTo7t+0c9i/eDwDQNdGhzxN9BFdERES+ioGE6mztq2stt5OmJiEoLEhgNURE5MsCRBfgC9LS0pCWllZjG4PBYLOs1+uRn5/vzrKEOpN1Bsd+PgYAiIiLQLtx7er98+r1ekiSxDMDKwy3qzJxuyqPq7epXq93qj0DiQMKCgpw9uxZpx4jSRIkSXJTRWJJkoQNMzZYlvu+1BfqQHW9f17r90yp750/4nZVJm5X5XH1NnX2ORhIHBAREYHmzZvX2MZgMCAnJ8eyrFKpFPufw8lfT+J89nkAQExCDDre29ElP6v5OZT83vkjbldl4nZVHldvU2efg4HEAampqUhNTa2xTVZWFpKSKiYE0+l0iIxU3mylkklC9uxsy/Its29BdEy0657/enehEt87f8btqkzcrsrjym2q0+mcas9BreSU/Yv348LOCwCApj2bIvHORMEVERGREjCQkMNM5Sasm7bOsnzz7JvZVUtERC7BQEIO2/nNTlw5fAUA0Cq5Fdre2lZwRUREpBQMJOSQ8pJyZLyeYVlm7wgREbkSAwk55I8v/kDB6QIAQIcRHdByQEvBFRERkZIwkFCtSvWlyJyVaVkePHOwwGqIiEiJGEioVps/2oyiy0UAgM7jO6NJtyaCKyIiIqVhIKEaFV8txsZ3NwIAVBoVUl5PEVoPEREpEwMJ1WjDuxtgyJfP09P9oe5o0KGB4IqIiEiJGEioWoXnC5H9kTwrqyZIg+TpyYIrIiIipWIgoWplzspEeXE5AKDX470Q2ZLTQxMRkXswkHi7334DOnWSrz0o90Qutv17GwAgSBeEpFeSankEERFR3TGQeDNJAl55BThwQL724Cm+M17PgKnMBADo90w/hDUK89hrExGR/2Eg8Wa//AJs3Srf3rpVXvaAy/svY/e3uwEA2mgt+j/b3yOvS0RE/ouBxFtJEjBtGqDRyMsajbzsgV6SddPXQTLJrzPopUHQRmrd/pqkXJr0dIT36wdNerrgSojImzGQeCtz74jRKC8bjR7pJTn3xzkc+OEAAEDXRIc+T/Rx6+tZ4xeXAkkStK+/Ds2hQ9C+/rpHdzuSe/HzSq4WILoAssPcO6JSVf0DPm4ccPvtQFQUEBEBREZWXFd3OzjY4Zde++pay+2bpt2EwNBAF/1Qtaj8xTVqlPzzk2/75RcE7NgBAPL1L78At90muCiqN35eFUmTno6QF19E8dtvA6NHe/z1GUi8kfXYkcoKCoCFC517vuBgh8LLyQtaHPvlKgAgqlkIevQPBk6fltfrdIDajR1q/OJSnuvBWtJooDIa5etp04ChQ/nl5ev4eVUeLwiZDCTexnrsiHl3TX0ZDMDly/KlupcFsBaTAcQBAFLOzYWmx4sVDVQqIDy8+lBTWy9NTb01Sv3ikiTAZJIvRmPFbX9ZPnEC2LoV5i2oMu92vPtuoG1bOeDyYntRqbz/d16pn1d/5wUhk4HE29TUO2Lts8+Azp2B/Hy51yQ/3/HbdoLOEbTH6ethpBEuoQv22DaQJPk5Cgrq9/NZ99aYg0pJif0vrlGjgBYtvOcL1tllsm/pUtEVeDeVSmwgqq3N5cv2P6+33w60aiX/MxUQIF87e7uuj6vvc5h/dn/lJSGTgcSbONo7otEAX38NZGc7/8siSUBxsU1QkXLzsPZvO4CTJQCAwffGQt3s7zWHmqKiuv2MDvTWWKxcWbfXIPJlkiR//n0t1HpoWgK3MQcUXwxU9X3tzZvth0wP95IwkHgTR3tH6vPLolIBoaHypWlTAMC+BXtx8eQmAECzXs2QOP+vtQed8vKKgFL52pnbIv/oajS2//lx2TXLKhXwyCPAoUNyb1FlajXQoQPwxRe2u7V4EXMhmS+GQHcyTzXhwV4SBhJvYe4dUasd+yOhVrvkl8VYZsS6aessy0NmD4HKkecLCABiYuRLXZlMQO/ewK5d9v8QqNVAx47A3LkVSd5VX6D+3D3rbj//LM8uXB2TCTh4UO6p40BI8RwNhUajvL327Kn58/q//8nty8srvuRru+1MW1c8zh2vrbRD2gX0kjCQeIvSUuDUKcf/YzGZ5CNgSkudOqy3sl3f7MLVI1cBAK1TWqPtLW3r/FxO+/VXYPv26tebTMC+fcCFC/zi8hXO7HbkQEjvoFJVBP6a/PwzsHNn9evNn9fLl/3z82re1eZLgaq8HFiyBLhyxX6g8vDnlIHEWwQHy2nUkbEVZrGx9Qoj5SXlyHg9w7LscO+IK/CLS5k8sduRPI+f19qpVHLPcYAPfa3+/DPw739Xv97Dn1Mfeuf8QMuW8sVD/vj8DxScKQAAdBjZAS37e+61+cWlQIJ2O5IH8POqPF4YMtVufXbyWoZCAzJnZ1qWh8wc4rkXt/7icoT5i0tp+2iVpj67Hcl78fOqTJVPT1IdD522BGAPid/K/igbRZflQ3c739cZjbs29tyLCxovQ25WzW7HwsJCy+3w8HDbx9RztyN5AD+vyuOlvZkMJH6o+GoxNr67EQCg0qiQ8nqKZwvgF5dy2dntaMrPhyRJ8vikyEhBhVGd8fOqPF4aMhlI/FDW21kwFBgAADdOvhEN2jfwfBH84iLyHfy8KouXhkwGEj9TeK4QW/65BQCgCdYgeXqy4IqIiMjjvDBkclCrn1k/az3Ki8sBAL2n9EZEiwjBFRERETGQ+JXc47nY/m95IrIgXRAGvTRIcEVEREQyBhI/kvF6Bkzl8iCmfqn9ENYoTHBFREREMgYSP3Fp3yXs+nYXACAkJgT9U/sLroiIiKgCA4mfSJ+eDlyfp2jgSwOhjdQKrYeIiMgaA4kfOLv1LA4skc++qmuqQ58pfQRXREREZIuBxA+sfXWt5Xby9GQEhgYKrIaIiKgqBhKFO7HuBI7/ehwAEN02GjdOvlFwRURERFUxkCiYJEk2vSMpr6dAE6QRVxAREVE1GEgU7MiqIziz6QwAoNENjdD5vs6CKyIiIrKPgUShJJNt78iQmUOg1nBzExGRd+I3lELtW7gPF3dfBAA0690MCaMTBFdERERUPQYSBTKWGbFu2jrL8s2zb5ZPlkREROSlGEgUaOecnbh69CoAoM2QNmh7S1vBFREREdWMgURhykvKkfF6hmV5yKwhAqshIiJyDAOJwmz911YUni0EACSMSkCLfi0EV0RERFQ7BhIFMRQakDU7S15QAYNnDBZbEBERkYMYSBRk84ebUZRTBADocl8XNO7aWHBFREREjmEgUYiiK0XY9N4mAIA6QI2U11OE1kNEROQMBhKF2PD2BhgKDACAG//vRsS0ixFcERERkeMYSBSg8FwhtvxzCwBAE6zBTdNuElwRERGRcxhIFGD9zPUoLykHAPR5og8imkcIroiIiMg5DCQ+Lvd4LrZ/uR0AEKQLwqCXBgmuiIiIyHkMJD4u/R/pMJWbAAD9n+2P0IahgisiIiJyHgOJD7u07xJ2f7cbABASE4L+qf0FV0RERFQ3DCQ+bN3UdYAk3x708iAERwSLLYiIiKiOGEh81NktZ3Fw2UEAQHizcPSe0ltwRURERHXHQOKj1r661nL7puk3ITAkUGA1RERE9cNA4oNOrD2B478dBwBEt43GjZNvFFwRERFR/TCQ+BhJkmx6R1LeSIEmUCOwIiIiovpjIPExh1cexpnNZwAAsZ1j0Xl8Z8EVERER1R8DiQ+RTLa9I4NnDoZaw01IRES+j99mPmTv93txac8lAEDzvs2RMCpBcEVERESuESC6AKpZxowMpL+WjuTpyZZJ0ADg5tk3Q6VSCayMiIjIdRhIvFjGjAykT0+Xb7+eYbm/zc1t0GZIG0FVERERuR532Xgp6zBS2ZBZQzxbDBERkZsxkHihmsIIABz75ZjniiEiIvIABhIvU1sYAYD06enImJFRYxsiIiJfwkDiRRwJI2YMJUREpCQMJF7CmTBixlBCRERKwUDiBeoSRswYSoiISAkYSLxA+mvpQh9PREQkGgOJF0h5PUXo44mIiERjIPECydOSkfJGSp0em/JGCpKnJbu2ICIiIg9jIPESdQklDCNERKQUDCRexJlQwjBCRERKwkDiZRwJJQwjRESkNAwkXqimUMIwQkRESsRA4qXshRKGESIiUioGEi9mCSUqhhEiIlK2ANEFUM2SpyUziBARkeKxh4SIiIiEYyAhIiIi4RhIiIiISDgGEiIiIhKOgYSIiIiEYyAhIiIi4RhIiIiISDgGEiIiIhKOgYSIiIiE40ytLqLX622Wd+/eLagS36XX6yFJElQqFXQ6nehyyEW4XZWJ21V5XL1NK38PVv6erIyBxEWOHz9uszxlyhRBlRAREXmfyt+TlXGXDREREQnHQEJERETCcZeNi4wYMcJmuW3bttyv6oTdu3fb7Ob69NNP0bVrV4EVkStwuyoTt6vyuGOb6vV6m900lb8nK2MgcZG4uDg8/vjjostQjK5du2LQoEGiyyAX43ZVJm5X5RGxTbnLhoiIiIRjICEiIiLhGEiIiIhIOAYSIiIiEo6BhIiIiIRjICEiIiLhGEiIiIhIOAYSIiIiEo6BhIiIiIRjICEiIiLhGEiIiIhIOAYSIiIiEo6BhIiIiITj2X7JK8TFxeG1116zWSbfx+2qTNyuyuMN21QlSZLk8VclIiIissJdNkRERCQcAwkREREJx0BCREREwjGQEBERkXAMJORxhYWF+OGHH/DEE09gwIABaNSoEQIDAxEREYHExEQ8+OCD+Omnn8Dx1soxadIkqFQqy+Uf//iH6JKojnbs2IHnn38eN954Ixo1aoTg4GA0b94cvXr1whNPPIHFixfDaDSKLpMcdPLkSUybNg2DBg1Cw4YNERgYCJ1Oh7Zt2+Kuu+7Cd999h7KyMs8UIxF50Pvvvy9ptVoJQK2XpKQk6c8//xRdMtXT6tWrq2zb1157TXRZ5KT8/Hxp0qRJkkqlqvWzm5ubK7pccsD7778vBQcH17o9ExISpD179ri9Hs5DQh51+PBhlJSUAACaN2+OW265BT179kRsbCxKSkqwefNmfPfdd9Dr9cjMzERKSgo2b96M2NhYwZVTXRQUFODRRx8FAISFheHatWuCK6K6uHr1Km677Tb88ccfAOTP7l133YVu3bohMjIShYWFOHLkCH799Vds27ZNcLXkiE8++QTPPvusZXnAgAEYNWoUWrZsiYKCAuzbtw9z5syBXq/HoUOHMHjwYOzZswdNmjRxX1FujzxEVv72t79JQ4cOlX755RfJaDTabXPy5EkpISHBks4feughD1dJrvLII49IAKSWLVtKqamp7CHxUbfddptl2z377LNScXFxtW3Pnj0rlZWVebA6clZRUZEUHh5u2aZffvml3XaXLl2SunTpYmn3zDPPuLUujiEhj5o1axZ+/vln3HrrrVCr7f/6tWrVCgsWLLAsL1iwAEVFRZ4qkVxk7dq1+PLLLwEAn332GcLDwwVXRHUxZ84c/PzzzwCAxx57DO+99x60Wm217Zs1a4aAAHa+e7MNGzagsLAQANC7d2/89a9/tduuUaNGePPNNy3L69evd2tdDCTkUTExMQ6169atGxISEgAARUVFOHr0qDvLIhcrKirCww8/DEmScO+992LEiBGiS6I6evvttwEAOp0Ob731luBqyBUuXbpkud2+ffsa21qv1+v1bqsJYCAhLxYREWG5XVxcLLASctbLL7+M48ePIyYmBh999JHocqiONmzYgIMHDwIARo8ebfOZJN9lPSbv8OHDNba1Xn/DDTe4rSaAgYS8VGlpqc0HoVWrVgKrIWds3LgRn3zyCQDgvffeQ+PGjQVXRHWVkZFhud23b18AwJIlSzBs2DA0adIEwcHBaNasGYYPH46vv/4a5eXlokolJ5gP8QWAP/74A1999ZXddpcvX8Yrr7wCAFCr1UhNTXVrXdzRR15p3rx5yM/PBwD06NHDvSO7yWVKSkowefJkmEwm3HzzzXjooYdEl0T1YD6qBgAaN26Mu+++G0uWLLFpc/78eZw/fx6rV6/GBx98gOXLl6NNmzaeLpWcoNVq8fnnn2P8+PEoLy/Hww8/jDlz5tgcZbN371588803KCwshE6nw1dffYWBAwe6tS4GEvI6ly9fxosvvmhZnjp1qsBqyBnTp0/HoUOHEBISgi+++EJ0OVRP58+ft9w2b9ugoCA8+OCDGDRoEAIDA7Fr1y589dVXuHr1Kvbs2YPBgwdj+/btDo8XIzHuvvtu/Pbbb5gyZQr27duHDRs2YMOGDTZtAgMD8eqrr+LRRx9Fy5Yt3V+UW4/hIXKSwWCQkpKSLIeZjRkzRnRJ5KAtW7ZIGo1GAiC98847Vda/9tprPOzXx1gffg9Aio6OlrZv316l3fnz56VOnTpZ2j366KMCqiVnmUwmad26ddKNN95Y7aRoUVFR0t///nepqKjI7fVwDAl5DZPJhMmTJyMzMxMAEB8fj//+97+CqyJHlJaWYvLkyTAajejRo4fb9zWTZ5hMJpvl9957DzfeeGOVdk2aNMG8efMsy3PmzEFBQYHb66O6y8nJwc0334zBgwfj5MmT+OCDD3Ds2DGUlpYiLy8Pv//+O4YNG4a8vDx8+OGHSElJwZUrV9xaEwMJeQVJkvC3v/0Nc+fOBQDExcXht99+Q3R0tODKyBEzZ87E3r17odFo8OWXX0Kj0YguiVzAeu6YsLAw3H///dW27datG/r16wcAMBgMVbr/yXsUFRUhKSkJ69atQ3R0NLKzs/H3v/8dbdu2RWBgICIjIzFkyBCsWrUKU6ZMAQBs2bIFTz75pFvrYiAh4SRJwuOPP26ZRKtFixZYu3YtWrduLbYwcsiuXbss81OkpqaiR48egisiV7H+h6BLly4ICgqqsX2vXr0st48dO+a2uqh+PvvsM8vh3M8991yNc5G8/fbbiIqKAiBPUnnhwgW31cVBrSSUJEmYMmUKPv/8cwDyOTLWrVuH+Ph4wZWRo+bMmYOysjKo1WoEBgZi5syZdttZz/K4fv16S7uEhASMHTvWI7WScxITE/H7778DACIjI2ttb92Gu2y818qVKy23hw4dWmPbsLAwDBgwAKtXr4bJZMLWrVsxcuRIt9TFQELCmMPIv/71LwDylNPr1q1Du3btBFdGzpAkCYA83mD27NkOPWbdunVYt24dAHnCLQYS79StWzfLbfNh+DWxbuNIgCExzp07Z7ntyHYy95AA7p2tlbtsSIjKYaRp06ZYt25drdMYE5Hn3HHHHVCpVACAPXv2oLS0tMb21vOWmE/9QN7HemzQ6dOna23/559/Wm43aNDALTUBDCQkyBNPPGEJI02aNMG6devQoUMHwVVRXXz44YeQJKnWy2uvvWZ5zGuvvWa5f9myZeKKpxq1aNECycnJAIBr167hu+++q7btrl27sHnzZgDyF567J9GiuuvSpYvltvlAguocPXoU2dnZAOTZWq3HCbkaAwl53JNPPonPPvsMgBxG0tPT+d8UkZey3g333HPPYceOHVXaXLx4ERMnTrQsP/XUUwgJCfFIfeS8CRMmWG5//fXX+M9//mO33YULFzBu3DjLKQFGjBjh1gnvOIaEPGrq1KmW85yoVCo8/fTTOHDgAA4cOFDj43r06IG4uDhPlEhEVvr3748XX3wRb7/9NnJzc9GvXz/85S9/sczUunPnTstMrYB8pA1nV/ZuQ4cOxT333IPFixdDkiT89a9/xbfffovRo0ejRYsWKC4uxh9//IFvv/0WeXl5AORdNe+//75b62IgIY/Kysqy3JYkCS+//LJDj/v6668xadIkN1VFRDV56623oNFo8Pbbb6O0tBRffvml5TB9a7fddhvmz58PrVYroEpyxnfffYeIiAjL5JMZGRk2J1O0lpCQgO+//97tBxxwlw0REdVq1qxZ2LZtG5588kkkJiYiPDwcWq0WcXFxGD9+PFavXo2ffvqJkxn6iODgYPznP//Bjh078PTTT6NXr16IiYlBQEAAQkND0bp1a9x999349ttvsXv3bnTv3t3tNakk8zF7RERERIKwh4SIiIiEYyAhIiIi4RhIiIiISDgGEiIiIhKOgYSIiIiEYyAhIiIi4RhIiIiISDgGEiIiIhKOgYSIiIiEYyAhIiIi4RhIiIiISDgGEiIiIhKOgYSIiIiEYyAhIiIi4RhIiIiISLj/B7JwCkzlxIBCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9Vdf981m9u+YCHTgQ/VraHEhGjhypE044QcOHD9fw4cPVr18/zZ49W9dff33Y63rqqaf8YWTQoEFasGCBunfv7n981qxZ+sUvfqHHHntMBw8e1MyZM7VkyZKQ1l1eXq4pU6bowIEDmjp1qmbMmBF2fYiOhgOqGrR0QNVbb0kXXhjz8tBOHXXLl2FIbnf4YaGlxxLxeIwuXaQTT5TsdslmO3Lb+H6weeG2Z73hr7ewUHIeeltv/Owif7857N6A204ZhzS3cIou+fNbcuRF/024zYHkzjvvjEgBHo9H999/v396zpw5AWGkwaOPPqr58+dr7dq1Wrp0qT788EP/OCMtqamp0YUXXqj169dr8uTJmjNnTkRqRvuFekCVzeZrt2uXtT/ErCbRtnwZhm/rQbhhoaW29fVm/0U+druUleX7yc5u/n4oj733njRzZui/909/iv7mfbTP9dfV6Jzq6ZIku735N2G73ZDXa9MzP5qu9zN2SYrum7Dp45AsWbJEu3fvliSNHz9ew4YNa7adw+HQLbfcohsO75h86aWXggaSuro6XXrppVq8eLFGjx6tuXPnsqsmjoR7kFwsDqhCZMRqy5fHExgM2rMVoqKi5WAca8nJR0JBsOAQSpBIT/eF+vaaNk369a99fRfsebLZpE6dpMsua//vRHRNHfWqUj5v/U3YbjeU5zyoy0a8Jim6b8KmB5L//Oc//vvnnntu0LbnnHNOs8sdzev16pprrtG8efM0ZMgQzZs3T5mZme0vFhHz5pu+b29eb2jtf/Qj6a67JIfDt5zdzn0z7rf24RbOlq9rr/X9H9TWti1IVFWF/O8WdenpoW+BaC1IxOP3prQ031atKVN8fddc3zb8bzz7LFszTWcYUn2lVHtQqjsk1R7+aXQ/ZfNzMiSFklcN2ZWy903peIsHkg0bNvjvn3rqqUHb9ujRQ3369NH27du1d+9e/1kzjRmGoRkzZuiVV17RgAED9OGHHyo3NzcqtSM09fW+U8xWr5bWrPHdFheHHkYkqbpa2rYtejUiNA37n1sKLXV1vrM4WmMYvmAxeXL0a26OzSY5neHtymiprdMpJZn+Thp9F1zg26p1ZFec4d8V5/Xa1KlT/O2KS1iGIXmqm4SIgIDR2nzD0+yqGwt145lNXskd/aNaTX8Zbdq0yX+/X79+rbbv16+ftm/f7l/26EBy22236ZlnnlG/fv00f/58devWLeRatm3bpm1t/NRbv359m5azGrdb+uKLI8Fj9Wpp/XpfoGiPtDQpN9e3md7r9f0Eux8vm9+txjB8z6/H4wsfseRwtBwewg0SGRm+AIXwXHih73iu116TXnmlXgcP+l6Xl1+erMsuY8tIAI+7aVgICBItzK87/Jg3xi+woOxSal7Uf4vpgeRQw5i1krp06dJq+86dOze7rCTNnTtXf/rTnyRJffv21V133dXsOi666CJd1Hgn92HPPPNMwAG27eFyuVRWVhaRdcWrykrpiy8cWrfOofXrfbcbN9pVX9967s7N9ergwdA/ER5/vEo//GHoL9CGwYBaCi1ery1ImLG1GHh862x+2cbrbS4geb22Nq6z+WUbBjlqbtkjf3vTehr/NCwbuL6mv7Ppc2lrdn07dthVUxP6QQu9enl17bW1cjoNZWUZh7c4GP6f7Owj02lpkTkeouHYE7TdBRdIEye6ZBiGbDabnE6n3G7fFxLL8NbJVlcmW32Z77auTKovk63ukH868PFDgdPe2J4eZcguIzlHRnKOlJRz+H4n361/uuF+JzkOrVTat38Mce1eVeWdrbowP9NcoWwubcT0QNK44LQQ4nV6err/fsVR7yoHGx0luXDhwhbX0bdv32YDSSQZhiHDQl/TDx2yacMGX+hYt86hDRsc+vpruwyj9U+I/v09Ovlk38+QIb5bp9PQwIHZKi+3BV2HzWYoJ8fQhRfWhr3Vw2YLtindOn0TT6ZNy9B77yX7D2ANxm43NHx4vX7969DfuC30kkp4jd/j4vK9zvDIVld+OCAcahogWpxf7pvvif1BSkZStrxHh4hmw0V2k8fkcIaV2Os6n6GULU/6/t4g74eGbDKSclTb48KwX4Dh/l+YHkgiafr06ZrecESdyWw2m2yR+Dpngn37bP7g0bDlY+vW1rdm2O2GBgzwBgSPwYM9yslprrVN//xnta66KkM2m9FsKLHZfP/M//hHtdLTE/O57GjOO69e77yTElJbr9em88+vT9jXSUfX0G9Re68zvFJ9eetbIpqEi8MhpD72m8GMpKxmAkOno7ZONAoYjaaVlCXZHG36vW169pPSVX3KP5Xx+VUyZGs2lBiH11x9yj9kS0pv8nirdYX5f2F6IHE6nf4tGzU1NXI6nUHbVzc6GCErKyuitdxwww0688wz27Ts+vXrNWvWLP+00+lUTvOfxHHDMHwHijY+3mPNGt8+4takpEiDB0tDh0rDhvl+Bg+2KSPDISm0F9UVV/j25bd8kJzt8EFynCGVKK67TrrjjtBPD502LYPjDhJYwy6bZt/rDEOqdwUeF9HM2R6qa2l+mWK+JdORIaV0klJyfbfJnQJvj54f0CZHNntS28KBWXIOvwl/Ol2qPShDdtnk9d/aUjpJo59VZu+2Hanc2uf50UwPJJ06dfIHktLS0lb/gP379wcsG0kFBQUqKCiI6DrbqmGUy7fekvbvlzp39o3t0NZRLr1e6dtvA890Wb1aIQ0HnJEhnXKKL3Q0BJBBg3yhpL04SM5aOD3UQgxD8lQ1HxZqDyq1Yo9UVyZ7fZmkqubP9jDCOJUuEhxpzYSITr4g0VyIaDw/OUdyROBNLdH0vlC6eJe07TXVb37F14cpuUrud7lUcJnvOY0R0wPJgAEDtHnzZknS5s2b1bdv36DtG9o2LGtFgaNc+sKE3S698UZoo1zW1UlffXUkdKxeLa1dG9rpmJ06BQaPYcOk447zneEQLWlpvkHPLrigKvg3LiQETg+NI56a1k8ZbWnrRd2hoGd6ROVjypZ0ZCtEKCHi6OARww9PS3GkSf2uUVXeBaa+B5seSAYPHuy/js3KlSs1ceLEFtvu3bvXf8pvt27dmpzyawVNR7kMvD16lMuaGmnDhsAtH+vXh3a0e/fuR0JHQwDp2zcyZzKgY2PLV4R4aoOfGtraLhBvjE97sTlC29XR0tYLR4SGlkVCMj2Q/OAHP9Af/vAHSb7RV2+//fYW286bN89/v7VRXRNRqKNcSr5dN8cfL23c6DuNsTXf+17gVo9hw6SePSNSNtCstOQaXXP6q/phr1el2gNSSp6S+02Vkqcq2tfEiBveet+xEK2FiJa2XsT8TA+bb9dFc2HhqBBRWZcsb1K2lNxJWXl9fPOTwjvTA2jM9EAyfvx49ejRQ3v27NGiRYu0evXqZq9n4/F49Je//MU/fcUVV8SyzJgI9foukm+47S++aP6x448P3PIxdKjvGBQgZna8LS2fLtUdVFKjA+W05x3p81ulMc9KbTxQLqYM75FAEc6ujob59eGNwxARydlh7upotPUiOUuyhTY+UH1ZmX/zvjLZxYr2Mz2QOBwO3XvvvbrpppskSdOmTdOCBQuajLD661//WmvXrpUknX766Tr77LNjVmNRUZGKioqCtnEftY+kLQOjvfpqhv/KqKExdOKJR06zHTLEo5NO8qi5k48SYYw2l+vIQEtIXEl75ynj86sl+U5HtMl7+L7v1qg7JC2ZoqoRL6i+e5S3dBqGVF8ReIpo3aFmThlt/nRS1VcEHaMhKiU7Mo86ZfToMSeaOZ00uZNvMKykLMnehrf1+sM/1aGfKsvr1Xoi3acxGxht8+bNevrppwPmNR4+fc2aNbr77rsDHp80aZImTZrUZF0zZszQm2++qY8++khffvmlhgwZohkzZmjQoEE6cOCAXnrpJRUXF0vynVnzr3/9q61lt0l5ebl27twZ1jJtGRjtwAGFEUakMWPqNW9eZTO/O6xfGzfifqAltM5To/S1P5GkFj/IbTJkyKb0tTepfPLG4AciHj7To+UQ0cJ9/7gU5f4gFCuGPa3RIFZNB7AKPuhVtmRPbmcBsXnt8Hq1nkj3acwGRtu6dasefvjhFh9fv359k+u7JCUlNRtIkpKS9Prrr+uqq67Su+++qz179ujBBx9s0q537956+eWXdeKJJ7a17DbJzs5Wfn5+0DZut1ulpaX+6bYMFpSXd+SMhNbY7Ya6dAl/4Jl4FvWBlhB1yXvmHj4NNDibDNnqDylj3Y9lZHyv+WG5G8KFUR+Dyo8wbMlNtj4EDmDV8uBWRlJ2u870SKT/el6v1hPpPk24gdEaZGVl6Z133tHcuXP13HPPaeXKldq3b5+ysrJ0zDHH6JJLLtHMmTNNORWpsLBQhYWFQdsUFxdr3Lhx/um2DIw2dar0zjuhtfV6bbr88mTLnR7Lab8Jpr5aqtohVe+QKrdLW/4a1uIpe+ZGvib/mR65zRwv0SnogZpK7iSbI40P2BDxerWeSPZpzAZGmzBhQlQ2002ZMkVTpkyJ+HoTwdSpvnFGQh3l8rLLYlUZOqSjw0b1Dt+0//52yb2/9fWEzRZ2iAgIHkmZnOkBJKC42UICRrlEDDUOG1WHw0VMwkZjNqnLKGnI7wIDRpIz5DM9AFgHgSTONB3l8shIrV6vGOUSrauvlqp3HgkZ0QgbtiQpI1/K6C1l9PHdpveWXP+Tvg51t40hHTdL6j6+fbUAsAQCSRxqPMrlm2/6zr7Jy5MuvliMctnRtRo2dkju0tbXE0xLYSOzz5Hb1G6SvZnrCXhqpM3P+8biCHq67OHdMgXsdwTgQyCJUw3Xd7nmGrMrQcw0CRs7mgaPSIaNo0NGa2EjFI4036BnS6bId85Ic6Hk8H7H0c9y7REAfgQSIBb8YaOZkJEoYSNUvS+QznirxUua6/AlzRNipFYAMUMgAdrLU9PyFo1Iho30Xs2HjFiGjVDF0SXNASQGAkkIYjV0fIflqVHy7reUsnOubHUHZSTnqip/iup6XmT+B5enRvaaXbLV7JS9eqfvfvVO2Wt2Hplf274DRA1bkoy0HvKm9ZY3rZeM9Hx503rJm5bvv2+kdvONr9GSWkm1Jlw3pTV5F8iVMtE/toHT6ZRcbkkxvgotIo6h460nYYeO70hiNXR8R5S0d54y1t0ke31Z4Kb90v/I++WvVDXkH6rvfk50fvnhsHF04LDX7JTt8G1EwkZqD3kbhYzGQSOksOFfWWL+PzHEuDXRr9aTsEPHdySxGjq+o0naO08Zq44ctXv0Rdhs9eXKXHV12y7C5qmRvWa3bDU7ZK9uFDKqd0Rwy4ZDRlrPZrZs9JKR3jvksGH1/xKGGLcm+tV6GDo+AcRq6PgOxVMjfTjr8ETLF2GTbMpcP8t3PELD7htPjVTVzKmvjc9OcZe0rz6bQ0rPP3KMRuNTYA/f2tK6y2Z3iCG8WscQ49ZEv1pPQg4dD7TLtleluoMhNDR8B0R+eJpvMkZhQ2nd4+cAUQDoAAgkMMeOtyTZpVAvDX9wTWjtGsJGcyGDsAEAcYtAgtip3i2VfCKVfiLt/kghh5EGhA0AsCwCCaLDWy8dWn84gCz3hZDKLW1cmV3q+QNp/NuEDQCwKAIJIsN9QCr91Bc8Sj+R9n8m1VdGaOVeqe+VhBEAsDACCcJneKXyTb7g0bAFpHxj8GUc6VLnkVKX06Sup0mdTpHmncxF2AAAkggkCEWdSzqw8sjxH6XLfWe+BJNR4AseXU6TuoyRcodI9uTANlyEDQBwGIEkBB1q6HjDkK16m5IOfibHwc+UdHCF7OVf+Acra3YRW7I8OSfLkztS9bmj5Ol0qoz0owaSq6hqumDWGUoa8YLS194ke/2hJhdh8yblqPqUf6g+6wwpEZ9LSGKIcauiX62HoeMTgKWHjve45Shf7wsghz5T0sHPZHfvCbqIN6WLL3x0Gqn63JHy5Jzi2yXTWIh/e123c1Q3eaOS98yVbedc2esOypucKyN/iup6TPFtGUmE5xEtYohxa6JfrYeh4xOAlYaOt9Xs9QcPx8HP5ChbK5u35QudGbLJmzXIFzxyR8qTO0rejH5So7+t3X9lUrrqe18hV6fzAy7CFn/PHtqCIcatiX61HoaOTwCmDB3vqfGNZrrjLcm9X0rtLPW+SCqYGvrxFF6PVLbBd8xHw/Efru+CL5OcLXUe7T/+w9Z5pBwpOYrV+S0MRW1N9Ks10a/Ww9DxCLTjbWn59MNDqzeMZmqXtr8hfX6r72DQ3hc0Xa720JFTb0s+kfavkOpb2YeXddyRM1+6jJGyB3F6LQAg5ggk8WbH29KSixrN8Abe1h3ynZky7k0pZ2DgwGNlXwZftyNNyjs18OyXtK6R/xsAAAgTgSSeeGp8W0YktTw2x+H5Sy8O0uaw9Hyp6+mNxv4YIjlSIlMrAAARRCCJJyFfAVdqEkZsSVLuUN9Wj4YtIJl9Il4iAADRQCCJJ+FeATethzTgVl8AyRshJWVEsTgAAKKHQBJP3PsV1hVwswdKJ/46auUAABArdrMLQCOpnRV6l9il1LxoVgMAQMwQSOJJ74sU+hYSr9T74igWAwBA7BBI4knBVCk5V62PfWqTUnK5Ai4AwDIIJPHEkeYb9ExSy6GEK+ACAKyHg1pDENOr/XbgK+By9VBrol+tiX61Hq72mwBifbXfxlfATd7znmx1B2Uk56qux3mWvgIuVw+1JvrVmuhX6+FqvwnAlKv9Hr4Cbn3vKwJmW/m7CFcPtSb61ZroV+vhar8JwJSr/XZQXD3UmuhXa6JfrcfMq/1yUCsAADAdgQQAAJiOQAIAAExHIAEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAmI5AAgAATEcgAQAApuPieiEoKipSUVFR0DZutztg2uVyqaysLJplWY7L5fJf2AnWQb9aE/1qPZHuU5fLFVZ7AkkIysvLtXPnzrCWMQxDhmFEqSJravyc8dxZB/1qTfSr9US6T8NdB4EkBNnZ2crPzw/axu12q7S01D9ts9n45hCmhueL585a6Fdrol+tJ9J9Gu46CCQhKCwsVGFhYdA2xcXFGjdunH/a6XQqJycn2qVZTsPmQp47a6FfrYl+tZ5I9qnT6QyrPQe1AgAA0xFIAACA6QgkAADAdAQSAABgOgIJAAAwHYEEAACYjkACAABMRyABAACmI5AAAADTEUgAAIDpCCQAAMB0BBIAAGA6AgkAADAdgQQAAJiOQAIAAExHIAEAAKZLMruARFBUVKSioqKgbdxud8C0y+VSWVlZNMuyHJfLJcMwZLPZzC4FEUS/WhP9aj2R7lOXyxVWewJJCMrLy7Vz586wljEMQ4ZhRKkia2r8nPHcWQf9ak30q/VEuk/DXQeBJATZ2dnKz88P2sbtdqu0tNQ/bbPZ+OYQpobni+fOWuhXa6JfrSfSfRruOggkISgsLFRhYWHQNsXFxRo3bpx/2ul0KicnJ9qlWU7D5kKeO2uhX62JfrWeSPap0+kMqz0HtQIAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAmI5AAgAATEcgAQAApiOQAAAA0xFIAACA6QgkAADAdAQSAABgOgIJAAAwHYEEAACYjkACAABMRyABAACmI5AAAADTEUgAAIDpCCQAAMB0BBIAAGA6AgkAADAdgQQAAJguyewCEkFRUZGKioqCtnG73QHTLpdLZWVl0SzLclwulwzDkM1mM7sURBD9ak30q/VEuk9dLldY7QkkISgvL9fOnTvDWsYwDBmGEaWKrKnxc8ZzZx30qzXRr9YT6T4Ndx0EkhBkZ2crPz8/aBu3263S0lL/tM1m45tDmBqeL547a6FfrYl+tZ5I92m46yCQhKCwsFCFhYVB2xQXF2vcuHH+aafTqZycnGiXZjkNmwt57qyFfrUm+tV6ItmnTqczrPYc1AoAAExHIAEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAmI5AAgAATEcgAQAApiOQAAAA0xFIAACA6QgkAADAdAQSAABgOgIJAAAwHYEEAACYjkACAABMRyABAACmI5AAAADTEUgAAIDpCCQAAMB0BBIAAGA6AgkAADAdgQQAAJiOQAIAAExHIAEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAmC7J7AISQVFRkYqKioK2cbvdAdMul0tlZWXRLMtyXC6XDMOQzWYzuxREEP1qTfSr9US6T10uV1jtCSQhKC8v186dO8NaxjAMGYYRpYqsqfFzxnNnHfSrNdGv1hPpPg13HQSSEGRnZys/Pz9oG7fbrdLSUv+0zWbjm0OYGp4vnjtroV+tiX61nkj3abjrIJCEoLCwUIWFhUHbFBcXa9y4cf5pp9OpnJycaJdmOQ2bC3nurIV+tSb61Xoi2adOpzOs9hzUCgAATEcgAQAApiOQAAAA0xFIAACA6QgkAADAdAQSAABgOgIJAAAwHYEEAACYjkACAABMRyABAACmI5AAAADTEUgAAIDpCCQAAMB0BBIAAGA6AgkAADAdgQQAAJiOQAIAAExHIAEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAaMXvV+jxvMe14vcrTPn9BBIAADq4xQ8u1vJHlkuGtPyR5Vr84OKY10AgAQCgA1v84GItundRwLxF9y6KeSghkAAA0EE1F0YaxDqUEEgAAOiAgoWRBrEMJQQSAAA6mFDCSINYhRICCQAAHUg4YaRBLEIJgQQAgA6iLWGkQbRDSVLU1gwAAExheA2VbS9T6Vel2r9pv/9284LN7VrvovsWafw94yNUZSACCQAACarWVavSTYGho/SrUu3/Zr/qq+sj/vsm3D8h4utsQCABACCONWztaAgbpZtKtf+r/SrdVKqKnRVhrSu9c7qSM5NVvq087DomPDAhaltHJAIJAABxodZVq/1fNw0d+78Ob2uHPcmu3GNy1WVAF3Ue2Nl3O8B3m9ElQ1L4x5JEO4xIBBIAAGLG8Boq31HuDx2Nd7OEvbUjL11dBh4OG41uc/vnypHsCLpsQ7gIJZTEIoxIBBIAACLOv7WjUejYv8k3Hc7WDpvDprxj8pqEjsZbO9oqlFASqzAiEUgAAGgT/9aOo0PHV6Uq3xHeMRqNt3Y0Dh25/XPlSAm+taM9goWSWIYRiUACAEBQtZVHju1oHDr2f71fdVV1Ia+n8daOxqGjy8D2b+1oj+ZCSazDiEQgAQBAhnHk2I6GXSsNB5WWbw9va0dabpo/bDQcVOo/tiOKWzvaY/w94+WucWv5b5drzB1jYh5GJAIJAKADadjacXTo2L8p/K0duf1zm4SOzgM6K6NLhmw2WxT/iugYdfsojfzlSNNqJ5AAAMK24vcr/N+mz3r4LLPLCdCwtaMhdDQ+k6VNWzsahY2G27xj8uJ2a0eiIpAAAMKy+MHFWv7IcknS8keWKzUt1ZRN/HVVdU3OZPEf21EZxtYO++GtHUefQjugizK6JubWjkRkuUCyevVqffzxx1q5cqVWrlyprVu3SpI2b96svn37mlscACS45gbUapiORigxDEMVOyuaho5N+1W2rSysdaV1Sms2dOQek6ukVMt9HCYcy/XAAw88oLlz55pdBgBYTrDRPdsbSuqq6rT/m/1NQkfpptI2be04OnR0GcjWjnhnuUAyZswYDR48WCNGjNCIESM0fPhw7d271+yyACChhTLUeGuhxDAMVeyqaBo6vipt09aO5kIHWzsSl+V67Ve/+pXZJQCApYRz3ZNF9y6St86rEy49oekVaL/er1pXbci/12a3qVO/Tk1GKO08oLMyu2WytcNiLBdIAACRE+5F2CRpyYNLtOTBJSG3T81JbTJuR+cBnZV3bB5bOzqQNve0x+PRxo0b9fnnn2vVqlX6/PPPtW7dOlVXV0uSrrvuOs2ePTvs9b799tuaM2eOVq5cqT179ig7O1vHHnusLr74Ys2cOVPZ2dltLRkAEIa2hJGW+Ld2HH0F2oFd2NoBSe0IJJdffrneeOONiBXicrl09dVX6+233w6YX1JSopKSEi1fvlx//etf9corr2j06NER+70AgKbKd5ZHJIxMfW2qugzswtYOtKpdW0gay8vLU+fOnfXNN9+0aV1Tp07V+++/L0nq3r27ZsyYoUGDBunAgQN66aWXtGzZMm3fvl3nnnuuli1bphNOOKGtpQMAGjG8hkr+W6Jtxdu0rXibti/brkNbDrV7vRMemKBBlw5qf4HoENocSEaOHKkTTjhBw4cP1/Dhw9WvXz/Nnj1b119/fdjreuqpp/xhZNCgQVqwYIG6d+/uf3zWrFn6xS9+occee0wHDx7UzJkztWRJ6PsnAQBH1FXXadfKXf7wsf2T7ao5VBPR32HGxdmQ2NocSO68886IFODxeHT//ff7p+fMmRMQRho8+uijmj9/vtauXaulS5fqww8/1FlnxddwxQAQjypLKrX9k+2+AFK8XbtW7ZK3ztti+6S0JOWPylfB2AIVjC3Q1qVbVfxIcci/jzCCtjB9h96SJUu0e/duSdL48eM1bNiwZts5HA7dcsstuuGGGyRJL730EoEEAI5iGIYOfHvAv/VjW/E27d+0P+gyGV0zVDC2QH1O76OCsQXqObRnwHVajv3BsUpKSwrpmBLCCNrK9EDyn//8x3//3HPPDdr2nHPOaXY5AOioPHUe7VmzJ+D4j8p9lUGX6Xx8Z/UZ6wsfBacXKO+4vFbPcmkIGcFCCWEE7WF6INmwYYP//qmnnhq0bY8ePdSnTx9t375de/fuVUlJibp27RrtEgEgbtSU1WjH8h3atsy3+2XHih2qr65vsb092a5ew3v5AsjpBepzWh9ldsts0+8OFkoII2gv0wPJpk2b/Pf79evXavt+/fpp+/bt/mUjGUi2bdumbdu2tWnZ9evXR6wOAGhQtq1M25Yd2fqxd/1eyWi5fWpOqvqc1sd//EevU3spOT05YvU0F0oII4gE0wPJoUOH/Pe7dOnSavvOnTs3u2yD9957Tw8++KB/+sCBA5Kkiy++WKmpqZKk8847T/fcc0+TZZ955pmAA2zbw+VyqawsvGszdHQul0uGYTBAksXQr6Hzerza/9/92vXpLu1asUu7Pt2lih0VQZfJ6pOl/DH56jmqp/JH56vzCZ1lsx95rqtqq6TQR2sPySm3nKKK8gqtemyVht82XKfccgrvdxYQ6deqy+UKq73pgaRxwWlpaa22T09P99+vqGj6Qi0pKdGKFSuazF+7dq3//sCBA8OsMnyGYcgwgnyNQRONnzOeO+ugX1tWV1Wnvav3+gLIp7u0+7Pdqq1oOT3Y7DZ1ObGLeo3upV6je6nnqJ7Kys9q0i4Wz/Mpt56iIbcMkc1mo18tItKv1XDXYXogibTp06dr+vTpZpchm83GN8IwNTxfPHfWQr8eUbmvUrs/3a1dK3Zp56c7VbKuRN76IKffZiSp54ie/gDSY0QPpWanxrDiltGv1hPpPg13HaYHEqfTqYMHD0qSampq5HQ6g7ZvuFaOJGVlNf1m0B433HCDzjzzzDYtu379es2aNcs/7XQ6lZOTE6nSOoyGzYU8d9bSEfvVMAzt37Tff/DptmXbdOCbA0GXcfZw+k+9LRhboO5DusuR7Ai6jJk6Yr9aXST7tLXP86OZHkg6derkDySlpaWt/gH79x85n75Tp04RraWgoEAFBQURXSeAjsFT69GuVY1GP122XVWlVUGX6XJCl4DxP3L757K1AR2W6YFkwIAB2rx5syRp8+bN6tu3b9D2DW0blgUAM1QfrPadfnt4/I9dK3epvqbl028dKQ71GtHLP/5Hn9P6KKNzRgwrBuKb6YFk8ODB/uvYrFy5UhMnTmyx7d69e/2n/Hbr1o0xSADEhGEYKtta5gsfh3fB7PtiX9Bl0nLTfON+HB7/o9eIXkpKM/0tF4hbpr86fvCDH+gPf/iDJN/oq7fffnuLbefNm+e/39qorgDQVt56r/au3xtw/EfFzuCn3+b2zw04/qPLwC4Bp98CCM70QDJ+/Hj16NFDe/bs0aJFi7R69epmr2fj8Xj0l7/8xT99xRVXxLJMABZW66rVjhU7/Md/7Fi+Q7WuIKffOmzqcUqPI8d/nF6grF6RPcge6GhMDyQOh0P33nuvbrrpJknStGnTtGDBAnXr1i2g3a9//Wv/WCKnn366zj777FiXCsAiKnZX+C88t614m/as3SPD0/KYCSnOFPUe3dt//EfvUb2V4kyJYcWA9bU5kGzevFlPP/10wLzGw6evWbNGd999d8DjkyZN0qRJk5qsa8aMGXrzzTf10Ucf6csvv9SQIUM0Y8YMDRo0SAcOHNBLL72k4mLfpa87deqkf/3rX20tu02KiopUVFQUtI3b7Q6YZqTW8DGipzWZ3a+G19CBrw/4Bx/b9ekulW0J/trM7JmpXqN7KX90vnqN6qUuJ3WRPcnuf7zaU63qsuoga7A+s/sVkZewI7Vu3bpVDz/8cIuPr1+/vsn1XZKSkpoNJElJSXr99dd11VVX6d1339WePXsChn9v0Lt3b7388ss68cQT21p2m5SXl2vnzp1hLcNIreFjRE9rinW/1tfUa9/afQGjn9YcrAm6TOcTOvuHXu81ppey+mQ1eVPmfzIQr1frYaTWw7KysvTOO+9o7ty5eu6557Ry5Urt27dPWVlZOuaYY3TJJZdo5syZpgzAk52drfz8/KBt3G63SktL/dOMXhg+Rn60prWPr/Vf82Tc3eMivv7qA9XavWK3/9ove1fvlafW02J7R6pDPYb1UK8xh4dfH9lTaZ1av2wFAvF6tR6zR2q1GUTbiCguLta4cUfebJcuXaqxY8eaWFHiKSsrY+RHi1n84OKIXhXWMAwd/O5gwPEfpRtLgy6T3jk9YPCxnsN6Kik1br6LJSxer9YT6T4N93ORVyWAqDg6jEhHLlkfaijx1nu1Z+0e/9kv24q3ybUn+H7pvGPzfAHk8PgfnQd05hs8kAAIJAAirrkw0iBYKHFXuLXj08On3xZv144VO1RXWdfi77En2dVzWE//1o8+p/eRs3t4188AEB8IJAAiKlgYadDw+NAbhvp3vWxftl171+2V4W15L3Jqdqp6j+ntH3wsf2S+kjOSI1g9ALMQSABETChhpMGiexe12ja7d7YKxh05/qPbSd1kd9iDLgMgMRFIAEREOGGkWTap++Du/sHHCk4vUE4BB0sCHQWBBEC7tTeMnHztyTrnr+coLYfTb4GOikASAkZqjQ1Gfkxci+5b1K7l1z+/XpP+OknuMnfrjREXeL1aT8KO1NqRMFJrbDDyY+IadfsorXh0RZuXH33HaPo8wfB6tR5Gak0AjNQaG4z8mHgMr6Gv3/xam17d1OZ1jLlzjEbdPiqCVSEWeL1aj9kjtRJIQlBYWKjCwsKgbY4ekc7pdDJ6YRsw8mNiMAxD3/7nWy24a4H2rN3T5vW0d+RWmIvXq/VEsk+dzvDGBCKQAAjLtmXbNP+O+dq2dFvA/L4T+2ryI5P1v4/+F9IBroQRAI0RSACEZM+6PVpw1wJ98943AfN7Du+pyb+drP5n9pfNZlPv0b0lKWgoIYwAOBqBBEBQB749oIX3LtQX//eF1OgYtc4DOmvSw5N0wiUnNNlX3BA2mgslhBEAzSGQAGhWxa4KLX5wsdY8tUbeeq9/fnafbE34zQQNmTZE9qSWR01tLpQQRgC0hEACIED1gWoVP1qsz/76meqr6/3zM7pkaNxd4zTixyOUlBbaW8f4e8bLXePW8t8u15g7xhBGALSIQAJAklRbWasVj6/Qst8vCxigLCUrRWNuG6MxPx+j1OzUsNc76vZRGvnLkZwaCiAoAgnQwXlqPVr1/1ZpyUNLVLm30j/fkerQqTedqrF3jFVm10wTKwTQERBIgA7K6/Fqw4sbtOi+RTq0+ZB/vs1u0ynXn6Lx941XTh/GlwAQGwSSEHAtm9jg2hixYRiGvpv3nT556BPt37g/4LHjphynMXeNUd7xeZIUkf9h+tWa6Ffr4Vo2CYBr2cQG18aIvh3FO7TsgWXaszJwdNWCSQU67Z7T1P2U7pIi+/zTr9ZEv1oP17JJAFzLJja4Nkb07F27V8seWKZtCwJHV+1xag+dfu/p6jOuT9R+N/1qTfSr9XAtmwTAtWxih2tjRFbpplItvGeh/vvqfwPmdz2xqyY9PEkDLhwQkw8T+tWa6Ffr4Vo2ACKqbHuZFt+/WGtnr5XhObLZtFPfTprwwAQNvmqw7I6WBzUDgFgjkAAWUlVapaW/XaqVf18pj9vjn5/ZPVNn3H2Ghv9ouBwpDhMrBIDmEUgAC3BXuLW8aLmWP7ZctRW1/vmpOak6/fbTNerWUUrJTDGxQgAIjkACJLD6mnp9/s/PtfThpaoqrfLPT0pL0shbRmrsr8YqPS/dxAoBIDQEEiABeeu9WvfcOi36zSKVby/3z7cn2TX0xqEaf894ZfXKMrFCAAgPgQRIIIZhaOMbG7Xw7oUq/ao04LGTrjxJEx+YqLxj80yqDgDajkACJADDMPTdx99pwZ0LtOvzXQGPHXfecZr08CT1GNLDpOoAoP0IJECc27Fih+bfMV9bFm4JmF8wtkCTfztZBWMLzCkMACKIQALEqX1f7tPCuxfqq7e+CpjffUh3TX5kso4951hGyARgGQQSIM4c2nJIi+5bpHVz1kmNLgWRe0yuJj44USf98CTZ7AQRANZCIAkBV/uNjY5+9dDKfZVa+ceVWv/v9fLWef3zM3tmatTto3TiNSfKkexQeUV5kLXEn47er1ZFv1oPV/tNAFztNzY66tVD3WVurfrrKq3951rVVdb556d2StWIn43QkBlDlJyRLCkxn5eO2q9WR79aD1f7TQBc7Tc2OtrVQ+ur67X2ybVaWbRS7kNHtrAlZSRp2E3DNOzmYUrrlGZihZHR0fq1o6BfrYer/SYArvYbOx3h6qGeOo/WPLNGSx5YoopdFf759mS7Rvx4hMbdNU7O7uFdJTPedYR+7YjoV+vhar9AB2B4DX3x8hdadO8iHfj2wJEHbNKQa4do/G/GK7dfrnkFAoCJCCRAlBmGoW//863m3zlfe9ftDXhs4EUDNfGhiep2YjeTqgOA+EAgAaJoW/E2zb9jvrYVbwuY33diX01+ZLJ6j+5tUmUAEF8IJEAU7Fm3RwvuWqBv3vsmYH7P4T01+beT1f/M/hwICACNEEiACDrw7QEtvHehvvi/LwIGNes8oLMmPTRJJ1x6AkEEAJpBIAEioGJXhRY/uFhrnlojb/2RQc2ye2drwv0TNGTaENmT7OYVCABxjkACtEP1gWoVP1qsz/76meqr6/3zM7pkaOydY3XqT05VUhovMwBoDe+UQBvUVtZqxeMrtOz3y+QuOzKoWUpWisbcNkZjfj5GqdmpJlYIAImFQAKEwVPr0ar/t0pLHlqiyr2V/vmOVIdOvelUjb1jrDK7ZppYIQAkJgIJEAKvx6sNL27QovsW6dDmQ/75NrtNp1x/isbfN145fRitEgDaikACBGEYhja9vUkL7lqgki9LAh4bdNkgTXxworoM7GJSdQBgHQQSoAVbFm3R/Dvma8enOwLmH3PWMZr0yCT1Gt7LpMoAwHoIJCEoKipSUVFR0DZutztg2uVyqaysLJplWY7L5fJf2MlMe9fu1bIHlmnbgsDRVXuM6KHT7z1dfc7oI0n0b4jipV8RWfSr9US6T10uV1jtCSQhKC8v186dO8NaxjAMGYbRekP4NX7OzHjuDn5zUJ88/Im+nfttwPzOAztrzD1j1P8c3+iq9Gt4zO5XRAf9aj2R7tNw10EgCUF2drby8/ODtnG73SotLfVP22w2vjmEqeH5ivVzV7GjQp8++qn+++J/ZXiOvICyC7I15s4xGjB1gOwOBjVrK7P6FdFFv1pPpPs03HUQSEJQWFiowsLCoG2Ki4s1btw4/7TT6VRODmddhKthc2Esnruq0iot/e1Srfz7SnncHv/8zO6ZOuPuMzT8R8PlSHFEvY6OIJb9itihX60nkn3qdDrDak8gQYfjrnBredFyLX9suWorav3zU3NSdfrtp2vUraOUkpliYoUA0PEQSNBh1NfUa+U/Vqr4kWJVlVb55yelJWnkLSM19ldjlZ6XbmKFANBxEUhged56r9Y9t06LfrNI5dvL/fPtSXYNvXGoxt8zXlm9skysEABAIIFlGYahja9v1MJ7Fqr0q9KAx0668iRNfGCi8o7NM6k6AEBjBBJYjmEY+u7j77TgzgXa9fmugMeOO+84TXp4knoM6WFSdQCA5hBIYCk7VuzQ/Dvma8vCLQHzC8YWaPJvJ6tgbIE5hQEAgiKQwBL2fblPC+9eqK/e+ipgfvch3TX5kck69pxjGSsBAOIYgQQJ7dCWQ1p03yKtm7NOajQoYO4xuZr44ESd9MOTZLMTRAAg3hFIEDdW/H6Flv92ucbcMUZnPXxW0LauvS4teWiJVv1rlbx1Xv/8rF5ZOuPeMzT0hqFyJDOoGQAkCgIJ4sLiBxdr+SPLJUnLH1mu1LRUjb9nfJN2NWU1+uQPn+jTP3+quso6//y03DSN/fVYjbx5pJIzkmNWNwAgMggkMN3iBxdr0b2LAuY1TDeEkrqqOn32t89U/Lti1Rys8bdLzkjW6J+P1mm/OE1pndJiVTIAIMIIJDBVc2GkwaJ7F8nwGHL2dGrJA0tUsavC/5g92a7hM4frjLvOkLNHeNdLAADEHwIJTBMsjPjb3L84cIZNGnLtEI3/zXjl9suNXnEAgJgikMAUoYSRow2YMkCTHpqkbid1i05RAADT2M0uAB1PW8KIJPUc3pMwAgAWRSBBTLU1jEi+Y0oWP7i49YYAgITDLpsQFBUVqaioKGgbt9sdMO1yuVRWVhbNshLSovsWtXv5U245JSK1IDZcLpcMw2CkXIuhX60n0n3qcrnCak8gCUF5ebl27twZ1jKGYcgwjNYbdjCj7xitTx/5tF3L87wmlsavBfrOOuhX64l0n4a7DgJJCLKzs5Wfnx+0jdvtVmnpkUvc22w2vjk0Y/Tto2WTzT8IWjjG3DlGo24fFYWqEE0NrwNeE9ZCv1pPpPs03HUQSEJQWFiowsLCoG2Ki4s1btw4/7TT6VROTk60S0tIZz18lio2V+iLl74IeZkJD0xoduRWJIaGzcC8JqyFfrWeSPap0xneGFEc1IqY+/aDb7Xx9Y0htyeMAID1EUgQU1uXbNXLF78sT61HktT95O5B2xNGAKBjIJAgZnas2KEXz3tR9dX1kqQTLj1BP1r1I014YEKz7QkjANBxEEgQE3vW7tELP3hBta5aSdJx5x6nS1+8VPYku8bfM75JKCGMAEDHQiBB1JVsLNGc789RzSHfVXr7Teqnqa9NlSPF4W8z/p7xGnPnGMnmO5uGMAIAHQtn2SCqDvzvgJ6b/JyqSqskSX1O66Mr5l6h5PTkJm1H3T5KI385klMIAaADYgsJoqZsW5mem/ycXLt9o/X1HNZTV827SinOFJMrAwDEGwIJosK1x6XnznxOZVt9w+d3PbGrrvngGqXlpJlcGQAgHhFIEHFVpVV67szndOCbA5KkvOPyNO3jacrokmFyZQCAeEUgQUTVHKrR82c/r5IvSyRJOd/L0bT50+TsEd6IfQCAjoVAgoipddXqhXNf0O7VuyVJWb2yNG3+NOX0CW0IYseiRcoaPVqORYuiViNij34FEAoCCSKirrpOL134knYs3yFJyuiaoWs/vlZ5x+SFtgLDUNr998uxaZPS7r9f4uqh1kC/WhZBE5FGIEG7eWo9evWyV7Vl4RZJUlqnNF374bXqekLX0Ffy4YdKWrNGkny3H34YhUoRc/SrNRE0LcnskEkgiXcffywNGuS7jUPeeq9ev+p1fTPvG0lSijNFV79/tXqc0iP0lRiGdM89Mhy+gdIMh0O65x7e5BId/WpdBE3riYOQycBo8cwwpDvvlDZu9N1OnizF0aBhhtfQ3Ovn+q/cm5SepCvfvVK9R/VueSGvV6qqkiorJZfLdzt/vrRypRr+MpvHI61cKd17rzRkyFG/tJkXydHzItUmmuuOtzbRWPfGjc33a2Ghr1/tdsnhOHLb+H648yK1jjh6fcWtRkHT5vH4bu+5RzrrLJ6/RNZcyDz77JiWQCCJZx9+6HsDl3y3sfoHMQyppsYXFhoHh0b3DZdL7/27ROs/8V2bxuEw9MPxu9X3n3dIj1W2vGx1deh1PPRQlP5AmOrPfza7gpbZbPERjOL5d65e3XzQfPxxacwY33PYEEwa7ofz09blzPidVhEnIZNAEq8O/4PI4ZA8Ht/t0f8gtbXNf/C3Nt1a28pK35aMlkqT9KHO1iqNkSTZ5NVlnpd17PubYvDEAFFkGFJ9vdlVJKaf/9zsCsyT6MHL5ZI2b24aMmO8lYRAEq8abx2RfKFk5Uqpa1ff/cpKqa7OlNIWaqI+PRxGJEMX6w0NVAthJCNDysyUnE7fbeP7GRm+Y2P2729+07/NJuXnS7feGpjSm0vsR88LpU1bl7Nqm0it2zCk++6Ttm5tPtjabFJBgfTLX/raejy+H6838DbUeeG2j/U6goR7WIRhWO/YqOa+BEcZgSQeNWwdsdma/pPv3x/535ea2jQsNDftdKp4VbqWfnSkpgtvLtDgKX9qfrn0dN8m3pZ88IH0f//X8uOGIe3YIQ0eHPN9mWiHDz6QNm9u+XHD8IWVY4/tGP1qGEeCSSKHK49Heuopac+elr9AdO0qXX75kfeutvw0PGfxvpxVfmd9vW9r+9FM2EpCIIlHR28dOVqvXlK3bs1vdWghSLT4WGamlBTav8GKv6zQ/I/e90+f89dzNPTmkW37G4/eJdUSE1I62oF+bcpmO3IsRnLTq1wnjA8+kB58sOXHDUPat086//yOETStwDCkUaN8xwU193qN8euUQBJvWntDdzh8uzFWrIjpG/nqp1fr/VuPhJHJv5uskW0NI1LroauBSfsy0Ub0qzURNK2ptddrjF+nBJIQFBUVqaioKGgbt9sdMO1yuVRWVhb270qaP1+ZIfyDVL75puonTw57/W3x1atf6f0fHQkjo345SoN/PLhNf58kyTCUeccdcthssoWw39Ww2eS54w5VjhrFm1w8o18tq9X3pQYmvD+hjRper4fPrGmxmcPR5tepy+UKqz2BJATl5eXauXNnWMsYhiEj3IOcDEOpDz3kP/WqxWYOh1Ifekh1EydG/Y3823e/1Qc//sB3ao2kobOGatQdo8L/2xpzu2XfsSOkDy1JshmG7Dt3ynC7fce7ID7Rr9bU8L4URtCM1fsT2i5p/nz/uCPB2DweJa1ZI8f8+WGHzHA/JwgkIcjOzlZ+fn7QNm63W6Wlpf5pm80mW5gvxqQFC8L6B0leuDCq30K2fLxF/7nhPzI8vn+qwTcM1hkPnRH239VEWppcCxfK1uj5kqTq6moZhiGbzab09PSAx4yuXWVLS2vf70V00a/WVFvbpqBpq6sjaMYrw1Daww+HFTLTHn5YlWEOzhn2Z2BYrTuowsJCFRYWBm1TXFyscePG+aedTqdycnJC/yWGIf3ud76zUkI5TdBuV+bvfiddfHFUvoVsWbRF717zrrx1vlpOvvZkXfTkRbLZI/S7mnluvGVl/g+urHCeO8QP+tWaVq2SSkoCZlVUVPjvZ2VlBTxm79ZNOd26xaQ0tIHbLe3a1fzZUs2wGYaSdu9WTnp6WCHT6XSGVRaBJF7U1krbtoU+ZoHXK23f7lsuwt9Cdny6Qy+e/6Lqa3wDRA26bJCmPDMlcmEEQGLp08f300jjoNlcEEUcS031HawaRshUt25R3+JFIIkXLfyDBBWFf5Dda3br+R88r7pK36Brx513nC554RLZk7gOIwBYRhyGTAJJPGnmHySWSv5boufPel7uMt8ZQ/0m9dPlr10uR4rDtJoAAB0DX3shSTrw7QE9d+ZzqiqtkiT1Oa2Prph7hZLSyKwAgOgjkEBl28r03OTn5NrtO2e85/CeumreVUpxpphcGQCgoyCQdHAVuyv03OTnVLbNN8hZt5O66ZoPrlFaDqdiAgBih0DSgVWVVmnOmXN04NsDkqTOx3fWtR9dq4zOGSZXBgDoaAgkHVTNoRrNOWuOSv7rO6sn53s5uvbja+XsEd554wAARAKBpAOqddXqhXNf0J41eyRJWb2ydN2C65TTh7EEAADmIJB0MHXVdXrpwpe0Y/kOSVJG1wxNmz9Nuf1zTa4MANCREUg6kHp3vV659BVtWbhFkpSWm6ZrP7pWXQZ2MbcwAECHRyDpILz1Xr1x1Rv69j/fSpJSnCm65v1r1GNID5MrAwCAQNIheD1evTX9LW18Y6MkKSk9SVe9d5XyRwa/gjEAALFCILE4wzD03k/e04YXNkiSHCkOXfHWFfreGd8zuTIAAI4gkFiYYRj64OcfaPWTqyVJNodNl71ymY456xiTKwMAIBCBxMIW3L1AKx5f4ZuwSZc8f4kGThloblEAADSDQGJRSx9ZquJHiv3TFz59oU664iQTKwIAoGUEEgv69PFPteCuBf7pc/52joZeP9TEigAACI5AYjGrn1qtD372gX/6zEfP1MhZI02sCACA1hFILGT9C+v1zo/e8U+fce8ZOv32002sCACA0BBILGLjmxv11nVvSYZvesxtYzThNxPMLAkAgJARSCzgm/98o9d++JoMjy+NjPjJCH3/D9+XzWYzuTIAAEJDIElwWxZt0SuXvCJvnVeSNOS6ITr3b+cSRgAACYVAksC2L9+uF89/UfU19ZKkQVMH6cKnLpTNThgBACQWAkmC2r16t1445wXVVdZJko477zhd8vwlsifRpQCAxMOnV5xb/OBi3W+/X4sfXOyft+/LfZpz1hy5y9ySpH6T++ny1y6XI8VhVpkAALRLktkFoGWLH1ysRfcukiT/7UlXnKQ5Z85R9f5qSVKf0/voirlXKCmNrgQAJC4+xeJU4zDSYNG9i7T8j8vlLvdtGek1opeueu8qpWSmmFAhAACRwy6bONRcGGnQEEa6ndRNV79/tdJy0mJYGQAA0UEgiTPBwkhjx557rDI6Z0S/IAAAYoBAEkdCDSOS9MnvPwk40BUAgERGIIkT4YSRBovuXUQoAQBYAge1hqCoqEhFRUVB27jd7oBpl8ulsrKykNa/4vcrtPyR5W2qbdG9i+SucWvU7aPatHw8cblcMgyDUWYthn61JvrVeiLdpy6XK6z2BJIQlJeXa+fOnWEtYxiGDMMIqe3y37YtjDRefuQvR7ZrHfGg8XMW6nOH+Ee/WhP9aj2R7tNw10EgCUF2drby8/ODtnG73SotLfVP22y2kFPmmDvGtHkLScPyVviW0vA3hPPcIf7Rr9ZEv1pPpPs03HUQSEJQWFiowsLCoG2Ki4s1btw4/7TT6VROTk5I6z/r4bOUmpYa9jEkkjThgQkaf8/4sJeLVw2bC0N97pAY6Fdrol+tJ5J96nQ6w2rPQa1xYvw94zXhgQlhLWO1MAIA6LgIJHEknFBCGAEAWAmBJM6EEkoIIwAAqyGQxKFgoYQwAgCwIgJJnGoulBBGAABWRSCJY/5QYiOMAACsjdN+49z4e8YTRAAAlscWEgAAYDoCCQAAMB2BBAAAmI5AAgAATEcgAQAApiOQAAAA0xFIAACA6QgkAADAdAQSAABgOkZqjRCXyxUwvX79epMqSVwul0uGYchms8npdJpdDiKEfrUm+tV6It2nR38OHv05eTQCSYR89913AdOzZs0yqRIAAOLP0Z+TR2OXDQAAMB2BBAAAmI5dNhFy/vnnB0z379+f/aphWL9+fcBurr///e86+eSTTawIkUC/WhP9aj3R6FOXyxWwm+boz8mjEUgipKCgQDfddJPZZVjGySefrLFjx5pdBiKMfrUm+tV6zOhTdtkAAADTEUgAAIDpCCQAAMB0BBIAAGA6AgkAADAdgQQAAJiOQAIAAExHIAEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB1X+0VcKCgo0H333RcwjcRHv1oT/Wo98dCnNsMwjJj/VgAAgEbYZQMAAExHIAEAAKYjkAAAANMRSAAAgOkIJIi5iooKvf7667r55pt12mmnqWvXrkpOTlZ2drYGDhyoadOm6f333xfHW1vH9OnTZbPZ/D+/+c1vzC4JbbRmzRr98pe/1NChQ9W1a1elpqYqPz9fI0aM0M0336zXXntNHo/H7DIRoi1btuiee+7R2LFj1aVLFyUnJ8vpdKp///665JJL9Pzzz6uuri42xRhADD322GNGWlqaIanVn3Hjxhlbt241u2S007x585r07X333Wd2WQhTWVmZMX36dMNms7X62j148KDZ5SIEjz32mJGamtpqfw4YMMDYsGFD1OthHBLE1Ndff62amhpJUn5+vs4880wNHz5c3bp1U01NjT799FM9//zzcrlcWrp0qSZMmKBPP/1U3bp1M7lytEV5eblmzpwpScrMzFRlZaXJFaEtDhw4oLPPPluff/65JN9r95JLLtGQIUOUk5OjiooKffPNN/roo4+0atUqk6tFKP72t7/ptttu80+fdtppuvDCC9WnTx+Vl5fryy+/1OzZs+VyubRp0yZNnDhRGzZsUI8ePaJXVNQjD9DIj3/8Y+Oss84yPvzwQ8Pj8TTbZsuWLcaAAQP86fz666+PcZWIlB/96EeGJKNPnz5GYWEhW0gS1Nlnn+3vu9tuu82orq5use3OnTuNurq6GFaHcFVVVRlZWVn+Pn3yySebbbdv3z5j8ODB/nY///nPo1oXx5Agph5++GF98MEH+v73vy+7vfl/v+9973t6+eWX/dMvv/yyqqqqYlUiImTBggV68sknJUlPPPGEsrKyTK4IbTF79mx98MEHkqSf/OQn+uMf/6i0tLQW2/fq1UtJSWx8j2fLli1TRUWFJOnUU0/VjTfe2Gy7rl276re//a1/esmSJVGti0CCmMrLywup3ZAhQzRgwABJUlVVlb799ttoloUIq6qq0owZM2QYhn74wx/q/PPPN7sktNGjjz4qSXI6nfrd735ncjWIhH379vnvH3fccUHbNn7c5XJFrSaJQII4lp2d7b9fXV1tYiUI1x133KHvvvtOeXl5evzxx80uB220bNkyffXVV5KkKVOmBLwmkbgaH5P39ddfB23b+PETTzwxajVJBBLEqdra2oAXwve+9z0Tq0E4PvnkE/3tb3+TJP3xj39U9+7dTa4IbbV48WL//VGjRkmS3njjDZ177rnq0aOHUlNT1atXL5133nn697//rfr6erNKRRgaTvGVpM8//1xPPfVUs+1KSkp05513SpLsdrsKCwujWhc7+hCXXnzxRZWVlUmShg0bFt0juxExNTU1uuGGG+T1ejV58mRdf/31ZpeEdmg4q0aSunfvrksvvVRvvPFGQJvdu3dr9+7dmjdvnv70pz9p7ty56tevX6xLRRjS0tL0z3/+U1dccYXq6+s1Y8YMzZ49O+Asmy+++ELPPvusKioq5HQ69dRTT+n000+Pal0EEsSdkpIS/epXv/JP33333SZWg3Dce++92rRpk9LT0/Wvf/3L7HLQTrt37/bfb+jblJQUTZs2TWPHjlVycrLWrVunp556SgcOHNCGDRs0ceJErV69OuTjxWCOSy+9VB9//LFmzZqlL7/8UsuWLdOyZcsC2iQnJ+uuu+7SzJkz1adPn+gXFdVzeIAwud1uY9y4cf7TzC666CKzS0KIPvvsM8PhcBiSjN///vdNHr/vvvs47TfBND79XpKRm5trrF69ukm73bt3G4MGDfK3mzlzpgnVIlxer9dYuHChMXTo0BYHRevUqZPxs5/9zKiqqop6PRxDgrjh9Xp1ww03aOnSpZKkY445Rs8884zJVSEUtbW1uuGGG+TxeDRs2LCo72tGbHi93oDpP/7xjxo6dGiTdj169NCLL77on549e7bKy8ujXh/arrS0VJMnT9bEiRO1ZcsW/elPf9L//vc/1dbW6tChQ5o/f77OPfdcHTp0SH/+8581YcIE7d+/P6o1EUgQFwzD0I9//GO98MILkqSCggJ9/PHHys3NNbkyhOKhhx7SF198IYfDoSeffFIOh8PskhABjceOyczM1DXXXNNi2yFDhmj06NGSJLfb3WTzP+JHVVWVxo0bp4ULFyo3N1crVqzQz372M/Xv31/JycnKycnRpEmT9N5772nWrFmSpM8++0w//elPo1oXgQSmMwxDN910k38Qrd69e2vBggXq27evuYUhJOvWrfOPT1FYWKhhw4aZXBEipfEXgsGDByslJSVo+xEjRvjv/+9//4taXWifJ554wn869y9+8YugY5E8+uij6tSpkyTfIJV79uyJWl0c1ApTGYahWbNm6Z///Kck3zUyFi5cqGOOOcbkyhCq2bNnq66uTna7XcnJyXrooYeabdd4lMclS5b42w0YMEBTp06NSa0Iz8CBAzV//nxJUk5OTqvtG7dhl038evfdd/33zzrrrKBtMzMzddppp2nevHnyer1auXKlLrjggqjURSCBaRrCyD/+8Q9JviGnFy5cqGOPPdbkyhAOwzAk+Y43eOSRR0JaZuHChVq4cKEk34BbBJL4NGTIEP/9htPwg2ncJpQAA3Ps2rXLfz+UfmrYQiJFd7RWdtnAFEeHkZ49e2rhwoWtDmMMIHbOOecc2Ww2SdKGDRtUW1sbtH3jcUsaLv2A+NP42KDt27e32n7r1q3++507d45KTRKBBCa5+eab/WGkR48eWrhwoY4//niTq0Jb/PnPf5ZhGK3+3Hffff5l7rvvPv/8t956y7ziEVTv3r01fvx4SVJlZaWef/75FtuuW7dOn376qSTfB160B9FC2w0ePNh/v+FEgpZ8++23WrFihSTfaK2NjxOKNAIJYu6nP/2pnnjiCUm+MLJo0SK+TQFxqvFuuF/84hdas2ZNkzZ79+7V1Vdf7Z++5ZZblJ6eHpP6EL6rrrrKf//f//63nn766Wbb7dmzR5dffrn/kgDnn39+VAe84xgSxNTdd9/tv86JzWbTrbfeqo0bN2rjxo1Blxs2bJgKCgpiUSKARsaMGaNf/epXevTRR3Xw4EGNHj1a1113nX+k1rVr1/pHapV8Z9owunJ8O+uss3TZZZfptddek2EYuvHGGzVnzhxNmTJFvXv3VnV1tT7//HPNmTNHhw4dkuTbVfPYY49FtS4CCWKquLjYf98wDN1xxx0hLffvf/9b06dPj1JVAIL53e9+J4fDoUcffVS1tbV68skn/afpN3b22WfrpZdeUlpamglVIhzPP/+8srOz/YNPLl68OOBiio0NGDBA//d//xf1Ew7YZQMAaNXDDz+sVatW6ac//akGDhyorKwspaWlqaCgQFdccYXmzZun999/n8EME0RqaqqefvpprVmzRrfeeqtGjBihvLw8JSUlKSMjQ3379tWll16qOXPmaP369TrllFOiXpPNaDhnDwAAwCRsIQEAAKYjkAAAANMRSAAAgOkIJAAAwHQEEgAAYDoCCQAAMB2BBAAAmI5AAgAATEcgAQAApiOQAAAA0xFIAACA6QgkAADAdAQSAABgOgIJAAAwHYEEAACY7v8H9T5nC734DbcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_context(\"poster\")\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df = pd.read_csv('./format_comparison_results.csv')\n",
+    "\n",
+    "# Define colors and markers for each format\n",
+    "format_styles = {\n",
+    "    'LEROBOT': ('red', '^'),\n",
+    "    'RLDS': ('purple', 'D'),\n",
+    "    'Fog-VLA-DM': ('blue', 'o'),\n",
+    "    \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n",
+    "    'HDF5': ('green', 's'),\n",
+    "}\n",
+    "\n",
+    "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n",
+    "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n",
+    "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n",
+    "\n",
+    "# Update the format_styles dictionary\n",
+    "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n",
+    "\n",
+    "# Get unique datasets and batch sizes\n",
+    "datasets = df['Dataset'].unique()\n",
+    "\n",
+    "# Create a figure for each dataset\n",
+    "for dataset in datasets:\n",
+    "    plt.figure(figsize=(6, 6))\n",
+    "    \n",
+    "    dataset_df = df[df['Dataset'] == dataset]\n",
+    "    \n",
+    "    # Create the line plot\n",
+    "    for format, (color, marker) in format_styles.items():\n",
+    "        data = dataset_df[dataset_df['Format'] == format]\n",
+    "        # Calculate throughput: (1 / loading time) * batch size\n",
+    "        throughput = (1 / data['AverageLoadingTime(s)']) * data['BatchSize']\n",
+    "        plt.plot(data['BatchSize'], throughput, \n",
+    "                 color=color, marker=marker, label=format, linewidth=2, markersize=8)\n",
+    "\n",
+    "    # Customize the plot\n",
+    "    # plt.xlabel('Num of Concurrent Reads')\n",
+    "    # plt.ylabel('Throughput (trajectories/s)')\n",
+    "    # plt.title(f'{dataset}')\n",
+    "    # plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    # plt.xscale('log')  # Use /log scale for x-axis\n",
+    "    plt.yscale('log')  # Use log scale for y-axis\n",
+    "    plt.tight_layout()  # Adjust layout to make room for the legend\n",
+    "    \n",
+    "    # Add a grid for better readability\n",
+    "    plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.savefig(f'./{dataset}_throughput.pdf')\n",
+    "    plt.show()\n",
+    "\n",
+    "# ... (rest of the existing code remains unchanged) ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "adc9dbca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                      Dataset   Format  \\\n",
+      "2   nyu_door_opening_surprising_effectiveness  LEROBOT   \n",
+      "3   nyu_door_opening_surprising_effectiveness     RLDS   \n",
+      "6   nyu_door_opening_surprising_effectiveness  LEROBOT   \n",
+      "7   nyu_door_opening_surprising_effectiveness     RLDS   \n",
+      "10  nyu_door_opening_surprising_effectiveness  LEROBOT   \n",
+      "11  nyu_door_opening_surprising_effectiveness     RLDS   \n",
+      "14  nyu_door_opening_surprising_effectiveness  LEROBOT   \n",
+      "15  nyu_door_opening_surprising_effectiveness     RLDS   \n",
+      "18  nyu_door_opening_surprising_effectiveness  LEROBOT   \n",
+      "19  nyu_door_opening_surprising_effectiveness     RLDS   \n",
+      "22                     berkeley_cable_routing  LEROBOT   \n",
+      "23                     berkeley_cable_routing     RLDS   \n",
+      "26                                     bridge  LEROBOT   \n",
+      "27                                     bridge     RLDS   \n",
+      "30                       berkeley_autolab_ur5  LEROBOT   \n",
+      "31                       berkeley_autolab_ur5     RLDS   \n",
+      "34                     berkeley_cable_routing  LEROBOT   \n",
+      "35                     berkeley_cable_routing     RLDS   \n",
+      "38                                     bridge  LEROBOT   \n",
+      "39                                     bridge     RLDS   \n",
+      "42                       berkeley_autolab_ur5  LEROBOT   \n",
+      "43                       berkeley_autolab_ur5     RLDS   \n",
+      "46                     berkeley_cable_routing  LEROBOT   \n",
+      "47                     berkeley_cable_routing     RLDS   \n",
+      "50                                     bridge  LEROBOT   \n",
+      "51                                     bridge     RLDS   \n",
+      "54                       berkeley_autolab_ur5  LEROBOT   \n",
+      "55                       berkeley_autolab_ur5     RLDS   \n",
+      "58                     berkeley_cable_routing  LEROBOT   \n",
+      "59                     berkeley_cable_routing     RLDS   \n",
+      "62                                     bridge  LEROBOT   \n",
+      "63                                     bridge     RLDS   \n",
+      "66                     berkeley_cable_routing  LEROBOT   \n",
+      "67                     berkeley_cable_routing     RLDS   \n",
+      "70                                     bridge  LEROBOT   \n",
+      "71                                     bridge     RLDS   \n",
+      "\n",
+      "    AverageTrajectorySize(MB)  \n",
+      "2                        0.88  \n",
+      "3                       16.76  \n",
+      "6                        0.88  \n",
+      "7                       16.76  \n",
+      "10                       0.88  \n",
+      "11                      16.76  \n",
+      "14                       0.88  \n",
+      "15                      16.76  \n",
+      "18                       0.88  \n",
+      "19                      16.76  \n",
+      "22                       0.68  \n",
+      "23                       3.23  \n",
+      "26                       0.31  \n",
+      "27                      15.58  \n",
+      "30                       0.00  \n",
+      "31                       0.00  \n",
+      "34                       0.68  \n",
+      "35                       3.23  \n",
+      "38                       0.31  \n",
+      "39                      15.58  \n",
+      "42                       0.00  \n",
+      "43                       0.00  \n",
+      "46                       0.68  \n",
+      "47                       3.23  \n",
+      "50                       0.31  \n",
+      "51                      15.58  \n",
+      "54                       0.00  \n",
+      "55                       0.00  \n",
+      "58                       0.68  \n",
+      "59                       3.23  \n",
+      "62                       0.31  \n",
+      "63                      15.58  \n",
+      "66                       0.68  \n",
+      "67                       3.23  \n",
+      "70                       0.31  \n",
+      "71                      15.58  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Update RLDS and LEROBOT average trajectory sizes\n",
+    "rlds_sizes = {\n",
+    "    'berkeley_cable_routing': 3.23,\n",
+    "    'bridge': 15.58,\n",
+    "    'nyu_door_opening_surprising_effectiveness': 16.76\n",
+    "}\n",
+    "\n",
+    "lerobot_sizes = {\n",
+    "    'berkeley_cable_routing': 0.68,\n",
+    "    'bridge': 0.31,\n",
+    "    'nyu_door_opening_surprising_effectiveness': 0.88\n",
+    "}\n",
+    "\n",
+    "# Update the DataFrame\n",
+    "for dataset in rlds_sizes.keys():\n",
+    "    df.loc[(df['Dataset'] == dataset) & (df['Format'] == 'RLDS'), 'AverageTrajectorySize(MB)'] = rlds_sizes[dataset]\n",
+    "    df.loc[(df['Dataset'] == dataset) & (df['Format'] == 'LEROBOT'), 'AverageTrajectorySize(MB)'] = lerobot_sizes[dataset]\n",
+    "\n",
+    "# Verify the changes\n",
+    "print(df[df['Format'].isin(['RLDS', 'LEROBOT'])][['Dataset', 'Format', 'AverageTrajectorySize(MB)']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "808066a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File Size (MB):\n",
+      "Format                                     Fog-VLA-DM  Fog-VLA-DM-lossless   HDF5  LEROBOT  RLDS\n",
+      "Dataset                                                                                         \n",
+      "berkeley_autolab_ur5                             1.85                25.57 281.55     0.00  0.00\n",
+      "berkeley_cable_routing                           0.18                 1.10   4.87     0.68  3.23\n",
+      "bridge                                           0.21                 4.40  29.91     0.31 15.58\n",
+      "nyu_door_opening_surprising_effectiveness        0.23                 5.78  79.54     0.88 16.76\n",
+      "\n",
+      "Relative Size (compared to Fog-VLA-DM):\n",
+      "Format                                     Fog-VLA-DM  Fog-VLA-DM-lossless   HDF5  LEROBOT  RLDS\n",
+      "Dataset                                                                                         \n",
+      "berkeley_autolab_ur5                             1.00                13.80 152.03     0.00  0.00\n",
+      "berkeley_cable_routing                           1.00                 6.14  27.14     3.79 18.02\n",
+      "bridge                                           1.00                21.16 144.02     1.49 75.02\n",
+      "nyu_door_opening_surprising_effectiveness        1.00                25.41 349.87     3.87 73.72\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate relative file size for each dataset\n",
+    "results = []\n",
+    "\n",
+    "for dataset in df['Dataset'].unique():\n",
+    "    dataset_df = df[df['Dataset'] == dataset]\n",
+    "    \n",
+    "    vla_size = dataset_df[dataset_df['Format'] == 'Fog-VLA-DM']['AverageTrajectorySize(MB)'].mean()\n",
+    "    \n",
+    "    for format in ['Fog-VLA-DM', 'RLDS', 'HDF5', 'LEROBOT', 'Fog-VLA-DM-lossless']:\n",
+    "        format_size = dataset_df[dataset_df['Format'] == format]['AverageTrajectorySize(MB)'].mean()\n",
+    "        relative_size = format_size / vla_size if vla_size != 0 else float('inf')\n",
+    "        \n",
+    "        results.append({\n",
+    "            'Dataset': dataset,\n",
+    "            'Format': format,\n",
+    "            'AverageTrajectorySize(MB)': format_size,\n",
+    "            'RelativeSize': relative_size\n",
+    "        })\n",
+    "\n",
+    "results_df = pd.DataFrame(results)\n",
+    "\n",
+    "# Pivot the results for easier reading\n",
+    "pivot_df = results_df.pivot_table(values=['AverageTrajectorySize(MB)', 'RelativeSize'], \n",
+    "                                  index='Dataset', \n",
+    "                                  columns='Format', \n",
+    "                                  fill_value='-')\n",
+    "\n",
+    "# Display the results\n",
+    "print(\"File Size (MB):\")\n",
+    "print(pivot_df['AverageTrajectorySize(MB)'].to_string(float_format='{:.2f}'.format))\n",
+    "print(\"\\nRelative Size (compared to Fog-VLA-DM):\")\n",
+    "print(pivot_df['RelativeSize'].to_string(float_format='{:.2f}'.format))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ca58a7db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter the data for batch size 8\n",
+    "batch_8_df = df[df['BatchSize'] == 8]\n",
+    "\n",
+    "# Get unique datasets\n",
+    "datasets = batch_8_df['Dataset'].unique()\n",
+    "\n",
+    "# Create a figure for each dataset\n",
+    "for dataset in datasets:\n",
+    "    plt.figure(figsize=(6, 6))\n",
+    "    \n",
+    "    dataset_df = batch_8_df[batch_8_df['Dataset'] == dataset]\n",
+    "    \n",
+    "    # Create the scatter plot\n",
+    "    for format, (color, marker) in format_styles.items():\n",
+    "        data = dataset_df[dataset_df['Format'] == format]\n",
+    "        plt.scatter(data['AverageTrajectorySize(MB)'], data['LoadingTime(s)'], \n",
+    "                    color=color, marker=marker, label=format, s=100)\n",
+    "        \n",
+    "        # Add labels for each point\n",
+    "        # for _, row in data.iterrows():\n",
+    "        #     if format == 'LEROBOT':\n",
+    "        #         plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n",
+    "        #                      xytext=(-40, -40), textcoords='offset points', ha='left', va='bottom')\n",
+    "        #     elif format == 'RLDS':\n",
+    "        #         # move to the left a little bit\n",
+    "        #         plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n",
+    "        #                      xytext=(-10, 10), textcoords='offset points', ha='left', va='bottom')\n",
+    "        #     elif format == 'HDF5':\n",
+    "        #         plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n",
+    "        #                      xytext=(-80, -10), textcoords='offset points', ha='left', va='bottom')\n",
+    "        #     elif format == 'Fog-VLA-DM-lossless':\n",
+    "        #         # move to very left \n",
+    "        #         plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n",
+    "        #                      xytext=(-80, 10), textcoords='offset points', ha='left', va='bottom')\n",
+    "        #     else:\n",
+    "        #         plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n",
+    "        #                      xytext=(5, 5), textcoords='offset points', ha='left', va='bottom')\n",
+    "\n",
+    "    # Customize the plot\n",
+    "    # plt.xlabel('Average Trajectory Size (MB)')\n",
+    "    # plt.ylabel('Loading Time (s)')\n",
+    "    # plt.title(f'{dataset} - Trajectory Size vs Loading Time (Batch Size 8)')\n",
+    "    # plt.legend()\n",
+    "    plt.xscale('log')\n",
+    "    plt.yscale('log')\n",
+    "    # for nyu_door_opening_surprising_effectiveness, move the x axis to the left\n",
+    "    if dataset == 'nyu_door_opening_surprising_effectiveness':\n",
+    "        plt.ylim(100, 1300)\n",
+    "    plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(f'./{dataset}_cost_vs_time.pdf')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "46a2410a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          Dataset               Format  Size (GB)\n",
+      "18    AutoLab UR5           Fog-VLA-DM       3.26\n",
+      "10    AutoLab UR5  Fog-VLA-DM-lossless      23.45\n",
+      "6     AutoLab UR5                 HDF5     258.33\n",
+      "14    AutoLab UR5              LEROBOT        NaN\n",
+      "2     AutoLab UR5                 RLDS      76.39\n",
+      "19         Bridge           Fog-VLA-DM       5.31\n",
+      "11         Bridge  Fog-VLA-DM-lossless     114.63\n",
+      "7          Bridge                 HDF5     779.24\n",
+      "15         Bridge              LEROBOT      16.34\n",
+      "3          Bridge                 RLDS     387.49\n",
+      "16  Cable Routing           Fog-VLA-DM       0.26\n",
+      "8   Cable Routing  Fog-VLA-DM-lossless       1.67\n",
+      "4   Cable Routing                 HDF5       7.38\n",
+      "12  Cable Routing              LEROBOT       0.36\n",
+      "0   Cable Routing                 RLDS       4.67\n",
+      "17   Door Opening           Fog-VLA-DM       0.10\n",
+      "9    Door Opening  Fog-VLA-DM-lossless       2.89\n",
+      "5    Door Opening                 HDF5      35.35\n",
+      "13   Door Opening              LEROBOT       0.38\n",
+      "1    Door Opening                 RLDS       7.12\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data = {\n",
+    "    'Dataset': ['Cable Routing', 'Door Opening', 'AutoLab UR5', 'Bridge'],\n",
+    "    'RLDS': [4.67, 7.12, 76.39, 387.49],\n",
+    "    'HDF5': [7.38, 35.35, 258.33, 779.24],\n",
+    "    'Fog-VLA-DM-lossless': [1.67, 2.89, 23.45, 114.63],\n",
+    "    'LEROBOT': [0.36, 0.38, None, 16.34],\n",
+    "    'Fog-VLA-DM': [0.26, 0.10, 3.26, 5.31]\n",
+    "}\n",
+    "\n",
+    "df_melted = pd.DataFrame(data)\n",
+    "\n",
+    "# Melt the DataFrame to have format and size as separate columns\n",
+    "df_melted = df_melted.melt(id_vars=['Dataset'], var_name='Format', value_name='Size (GB)')\n",
+    "\n",
+    "# Sort the DataFrame by Dataset and Format\n",
+    "df_melted = df_melted.sort_values(['Dataset', 'Format'])\n",
+    "\n",
+    "print(df_melted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b4ea3eb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Datasets in df_melted: ['AutoLab UR5' 'Bridge' 'Cable Routing' 'Door Opening']\n",
+      "Datasets in batch_8_df: ['nyu_door_opening_surprising_effectiveness' 'berkeley_cable_routing'\n",
+      " 'bridge']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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OuPnmAz/24IMRf/d3EWvWRHz5yxEnnXTw5wOAPPiRTYHcd1/EBRfsvytSz/33R7zrXRE/+tFBGQsAcieQFMRDD0VcemnE737XXP3evRHnnLN/HQB0OoGkIC65JGJkpLU1e/dGfPrTuYwDAAeVQFIAW7dGPPBAe2vvv3//C2ABoJMJJAXw93+fdj0ApCaQFECjF7HmvR4AUhNICmDPnrTrASA1gaQAFixIux4AUhNICuCEE9KuB4DUBJIC+OQn064HgNQEkgI44YSIU05pb+2aNf62DQCdTyApiK98JWLu3NbWzJ+//2/aAECnE0gK4qSTIq65JuLVr26ufv78iBtv9Af2AJgZBJICWbs24n/8j4jf+736dWvWRNxzT8QHPnBw5gKAvM1JPQDjHXtsxPXXRzz55P53YN26df/7jCxYsP+1Jp/8pNeMADDzCCQFdfzxEddem3oKADg4/MgGAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASG5GBZKXXnop7rjjjrj44otj1apVccghh8S8efPiTW96U2zcuDGeeuqp1CMCAJOYk3qALN1zzz1x+umnR0TEsmXL4g/+4A8iIuLnP/95XHfddfHd7343br/99jj55JNTjgkATDCj7pDMmjUrzjnnnHjggQfi6aefjptuuiluuumm+I//+I84//zzY3h4OD7ykY/ESy+9lHpUAGCMGRVITj311LjxxhsPuAMyb968uO6666K3tzeeeuqpeOCBBxJNCABMZkYFknrmz58fb37zmyMi4r/+678STwMAjNV2INm3b1889thjcf3118enPvWpOPnkk+M1r3lNlEqlKJVKcf7557fV95ZbbokNGzbEihUrYt68ebFo0aI45ZRT4uqrr47h4eF2x419+/bFk08+GRERS5YsabsPAJC9tl/U+kd/9Efxz//8z5kNUqlU4k/+5E/illtuGff8rl27YteuXfHggw/G1772tfjBD34Q73jHO1ruf8MNN8SuXbvisMMOi1NOOSWrsQGADEzrDslYhx56aBx55JFt99qwYUMtjCxevDguv/zy+N73vhdf//rXY82aNRER8cwzz8QZZ5wRv/rVr1rq/+STT8all14aERFf+MIXYu7cuW3NCQDko+07JG9729viLW95S6xevTpWr14dK1eujOuvvz4+/vGPt9zrm9/8Ztx+++0REXHMMcfE3XffHYsXL659fOPGjfGZz3wmrrnmmnj++efjwgsvjHvvvbep3sPDw7F+/frYvXt3bNiwIS644IKW5wMA8tV2ILnssssyGWDfvn3xN3/zN7XrG264YVwYGXXVVVfFT3/609i6dWvcd999cccdd9TeZ2Qqe/fujQ996EOxbdu2eM973hM33HBDJjMDANlK/ls29957bzz77LMREbFu3bp461vfOmnd7Nmz4+KLL65db9q0qW7fl156Kf7wD/8w7rnnnnjHO94RN998sx/VAEBBJQ8kP/7xj2uPzzjjjLq173//+yddN9Err7wSH/vYx2Lz5s1x/PHHx+bNm+OQQw6Z/rAAQC6SB5Jf/OIXtccnnXRS3dolS5bEsmXLIiJi586dsWvXrgNqqtVqXHDBBfGDH/wgjjrqqLjjjjvida97XbZDAwCZSv63bB5//PHa45UrVzasX7lyZTzzzDO1tYcddti4j1966aXx7W9/O1auXBk//elPY9GiRU3P8vTTT8fTTz/ddP1Y27Zta2sdAFCAQPLCCy/UHi9cuLBh/etf//pJ10ZE3HzzzfHlL385IiJWrFgRf/3Xfz1pj7POOivOOuusA57/9re/Pe4FttNRqVRiaGho3HW1Wo1SqVR3TaOaTleUPeY5R5a9p9OrnbWtrGm2tlFdUc5E3oqwz7xnyKp/N5z7dmftJJVKpaX65IFk7MDz5s1rWD9//vza4z179oz72PPPP197/LOf/WzKHitWrJg0kGSpWq1GtVqd9Hrs81Otmaqm0xVlj3nOkWXv6fRqZ20ra5qtbVRXlDORtyLsM+8ZsurfDee+3Vk7Sat7Sh5IsnT++ee3/Zb1WRt9C/2x15M9P3FNo5pOV5Q95jlHlr2n06udta2saba2UV1RzkTeirDPvGfIqn83nPt2Z+0kre4peSApl8u1Oxt79+6Ncrlct/7FF1+sPV6wYEGms3ziE5+I0047ra2127Zti40bN9auy+Vy9Pb2jqsZvTU38flWazpdUfaY5xxZ9p5Or3bWtrKm2dpGdUU5E3lruM/nH43439+IeH5rxEt7Il61IOJ1J0S86cKI1x1/cGYoSP9uOPftztopGn0/nyh5IHnta19bCySDg4MNN/Dcc8+NW5ul5cuXx/LlyzPtCdDQcw9FPHxJxOADB35s8MGI//fvIg5bE/HWL0e8vv5vI0KnSv5rv0cddVTt8fbt2xvWj60ZuxagI+34UcRd75o8jIy16/79dTt+dHDmgoMseSA57rjjao8feuihurU7d+6s/crvokWLDviVX4CO8txDEQPnROzb21z9vr3765+r/7USOlHyQPK+972v9rjeu69GRGzevLn2uNG7ugIU3sOXNB9GRu3bG/HIp3MZB1JKHkjWrVsXS5YsiYiILVu2xCOPPDJp3b59++KrX/1q7frcc889KPMB5OL5rY1/TDOVXffvfwEszCDJA8ns2bPjiiuuqF2fd9558dvf/vaAus9+9rOxdevWiIhYs2ZNnH766QdrRIDs/e+/T7seCqbt37LZvn17fOtb3xr33Ni3T//3f//3uPzyy8d9/NRTT41TTz31gF4XXHBB3HTTTXHnnXfGL3/5yzj++OPjggsuiGOOOSZ2794dmzZtioGBgYjY/5s13/jGN9odG6AYnt+adj0UTNuB5KmnnoovfOELU35827ZtB/x9lzlz5kwaSObMmRP/9E//FB/96Efjtttui9/85jdx5ZVXHlD3xje+Mb7//e/Hscce2+7YAMXw0p7GNfW8PM31UDDJf2QzasGCBXHrrbfGD3/4wzj77LNj2bJlMXfu3Fi4cGG8/e1vj6uuuioee+yxOOWUU1KPCjB9r5rmGzvOyfaNISG1tu+QvPvd787lvffXr18f69evz7wvQKG87oT9b3o2nfUwgxTmDglAV3nTJ9Ouh4IRSABSeN0JEQvb/BH0YWsy+9s2UBQCCUAqq78SMXtea2tmz9//N21ghhFIAFJ5/UkR7/zH5kPJ7PkR77zRH9hjRkr+1347QX9/f/T399etGRkZGXddqVRiaGho3PXon5meSjM1na4oe8xzjix7T6dXO2tbWdNsbaO6opyJvE25z/I7I07+acSvro7Y/e9TNzj09yLe8hcR5WMjxnxtyWSGjGTVvxvOfbuzdpJKpdJSvUDShOHh4dixY0dLa6rV6rjfQhp7PdVvJzVT0+mKssc858iy93R6tbO2lTXN1jaqK8qZyFvdffYcE/H2f4gY/l8Rz/xzxJ7/FbHv/0bMfk3EgjdHLDs7oufNo43ymSEDWfXvhnPf7qydpNU9CSRN6OnpiaVLl9atGRkZicHBwdp1qVQal3pHH098fqxmajpdUfaY5xxZ9p5Or3bWtrKm2dpGdUU5E3lrap+9R0X0/re0MxSgfzec+3Zn7SSt7kkgaUJfX1/09fXVrRkYGIi1a9fWrsvlcvT29o6rGb01N/H5Vms6XVH2mOccWfaeTq921rayptnaRnVFORN5K8I+854hq/7dcO7bnbVTlMvlluq9qBUASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAguTmpB+gE/f390d/fX7dmZGRk3HWlUomhoaFx19VqNUql0pQ9mqnpdEXZY55zZNl7Or3aWdvKmmZrG9UV5UzkrQj7zHuGrPp3w7lvd9ZOUqlUWqoXSJowPDwcO3bsaGlNtVqNarU66fXY56daM1VNpyvKHvOcI8ve0+nVztpW1jRb26iuKGcib0XYZ94zZNW/G859u7N2klb3JJA0oaenJ5YuXVq3ZmRkJAYHB2vXpVJpXOodfTzx+bGaqel0RdljnnNk2Xs6vdpZ28qaZmsb1RXlTOStCPvMe4as+nfDuW931k7S6p4Ekib09fVFX19f3ZqBgYFYu3Zt7bpcLkdvb++4mtFbcxOfb7Wm0xVlj3nOkWXv6fRqZ20ra5qtbVRXlDORtyLsM+8ZsurfDee+3Vk7Rblcbqnei1oBgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABIbk7qATpBf39/9Pf3160ZGRkZd12pVGJoaGjcdbVajVKpNGWPZmo6XVH2mOccWfaeTq921rayptnaRnVFORN5K8I+854hq/7dcO7bnbWTVCqVluoFkiYMDw/Hjh07WlpTrVajWq1Oej32+anWTFXT6YqyxzznyLL3dHq1s7aVNc3WNqorypnIWxH2mfcMWfXvhnPf7qydpNU9CSRN6OnpiaVLl9atGRkZicHBwdp1qVQal3pHH098fqxmajpdUfaY5xxZ9p5Or3bWtrKm2dpGdUU5E3krwj7zniGr/t1w7tudtZO0uieBpAl9fX3R19dXt2ZgYCDWrl1buy6Xy9Hb2zuuZvTW3MTnW63pdEXZY55zZNl7Or3aWdvKmmZrG9UV5UzkrQj7zHuGrPp3w7lvd9ZOUS6XW6r3olYAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhuTuoBOkF/f3/09/fXrRkZGRl3XalUYmhoaNx1tVqNUqk0ZY9majpdUfaY5xxZ9p5Or3bWtrKm2dpGdUU5E3krwj7zniGr/t1w7tudtZNUKpWW6gWSJgwPD8eOHTtaWlOtVqNarU56Pfb5qdZMVdPpirLHPOfIsvd0erWztpU1zdY2qivKmchbEfaZ9wxZ9e+Gc9/urJ2k1T0JJE3o6emJpUuX1q0ZGRmJwcHB2nWpVBqXekcfT3x+rGZqOl1R9pjnHFn2nk6vdta2sqbZ2kZ1RTkTeSvCPvOeIav+3XDu2521k7S6J4GkCX19fdHX11e3ZmBgINauXVu7LpfL0dvbO65m9NbcxOdbrel0RdljnnNk2Xs6vdpZ28qaZmsb1RXlTOStCPvMe4as+nfDuW931k5RLpdbqveiVgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDk5qQeoBP09/dHf39/3ZqRkZFx15VKJYaGhsZdV6vVKJVKU/ZopqbTFWWPec6RZe/p9GpnbStrmq1tVFeUM5G3Iuwz7xmy6t8N577dWTtJpVJpqV4gacLw8HDs2LGjpTXVajWq1eqk12Ofn2rNVDWdrih7zHOOLHtPp1c7a1tZ02xto7qinIm8FWGfec+QVf9uOPftztpJWt2TQNKEnp6eWLp0ad2akZGRGBwcrF2XSqVxqXf08cTnx2qmptMVZY95zpFl7+n0amdtK2uarW1UV5Qzkbci7DPvGbLq3w3nvt1ZO0mrexJImtDX1xd9fX11awYGBmLt2rW163K5HL29veNqRm/NTXy+1ZpOV5Q95jlHlr2n06udta2saba2UV1RzkTeirDPvGfIqn83nPt2Z+0U5XK5pXovagUAkhNIAIDkBBIAIDmBBABITiABAJITSACA5PzaLwB0iRt/eWNcseWK2DOyp+W1C+YuiCt//8o455hzcphMIAGArnHFlivi14O/bm/xnojP/exzuQUSP7IBgC7Rzp2RLNfXI5AAAMkJJABAcgIJAJCcQAIAJCeQAADJCSQAQHICCQCQnEACACQnkAAAyQkkAEByAgkAkJxAAgAkJ5AAQJdYMHdB0vX1zMmtMwBQKFf+/pXxuZ99rq2/2rtg7oK48vevzGGq/QQSAOgS5xxzTpxzzDmpx5iUH9kAAMkJJABAcgIJAJCcQAIAJCeQAADJCSQAQHICCQCQnEACACQnkAAAyQkkAEBy3jq+Cf39/dHf31+35sUXXxx3/S//8i9RqVTGfbxarUapVIr58+dP2aNRTacryh7znCPL3tPp1c7aVtY0W9uorihnIm9F2GfeM2TVvxvOfbuzdpLHHnts3PXY74mTEUiaMDw8HDt27GhpzV/8xV/kNA0AdJ4nnnii7scFkib09PTE0qVL69ZUKpUYGho6SBMBwMxSqlar1dRDzATXXXddbNy4MfUYAFBI1157bVx00UVTftwdkoyceeaZ466POOKIKJfLtesPf/jDMTg4GAsXLoybbrrpgPXbtm0bF2iuvfbaWLVqVX4DJ9Lov8NMmCPL3tPp1c7aVtY0W1uvrlvOfUQxzn7eM2TVf6af+4juOPuVSiXOPffcGBoait7e3gO+T04kkGRk+fLldZPf3Llza//7zne+s2G/VatWNVXXaVr979CJc2TZezq92lnbyppma1vpOVPPfUQxzn7eM2TVv9vOfcTMPfvlcjmGhoaiXC7H8uXL69b6tV8AIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCS8z4kB0lfX18MDw9HT09P6lGSKsp/hzznyLL3dHq1s7aVNc3WFuX/89SK8N8h7xmy6u/czxyt/Hfw1vEFMTAwEGvXrq1d33fffTPyTXJgLOeebuXsH8iPbACA5AQSACA5gQQASE4gAQCS81s2BbF8+fL4/Oc/P+4aZjrnnm7l7B/Ib9kAAMn5kQ0AkJxA0kUeeeSR+NKXvhQbNmyIFStWRKlUilKpFE8++WTq0WDafve738UXv/jFOPbYY2P+/Plx2GGHxdlnnx2PPPJI6tEgFzPta7of2XSRs846K26++eYDnt++fXusWLHi4A8EGfnd734Xp59+emzZsiUWLVoU69ati2effTYGBgbiVa96Vdx6661x+umnpx4TMjXTvqZ7UWsXOfnkk+O4446LE088MU488cRYvXp17Ny5M/VYMG1XXXVVbNmyJU466aS46667am9TvWnTpvjoRz8aH/vYx+KJJ56IBQsWJJ4UsjPTvqa7Q9LFlixZEjt37uzYNA0RES+//HIsXrw4du/eHQ899FCceOKJ4z7+gQ98IDZv3hxf+cpX4s///M8TTQn56/Sv6V5DAnS0+++/P3bv3h0rV648IIxERPzxH/9xRMSkt7aB4hBIJti3b1889thjcf3118enPvWpOPnkk+M1r3lN7cVC559/flt9b7nlltoLj+bNmxeLFi2KU045Ja6++uoYHh7OdhOQkyJ+fmzdujUiIlavXj3px9/61rdGRMSjjz7a1mxQxHM/I1UZ5+yzz65GxJT//Omf/mlL/fbs2VP90Ic+VLfnsmXLqg8++GA+G6pj8eLF1Yiobt++/aD/u+lMRfz8+PSnP12NiOoll1wy6ceff/75Wq89e/a0NB9Uq8U895Pp9K/p7pBMsG/fvnHXhx56aBx55JFt99qwYUPccsstERGxePHiuPzyy+N73/tefP3rX481a9ZERMQzzzwTZ5xxRvzqV7+a3vCQsyJ+flQqlYiIOOSQQyb9eLlcrj3es2dPW7PS3Yp47mciv2Uzwdve9rZ4y1veEqtXr47Vq1fHypUr4/rrr4+Pf/zjLff65je/GbfffntERBxzzDFx9913x+LFi2sf37hxY3zmM5+Ja665Jp5//vm48MIL4957752013nnnRc///nPW/r3f/jDH44vfvGLLc8NUynq5wfkybk/SFLfoukE//AP/9DyrbmXX365+oY3vKG27uGHH56y7oQTTqjV/eQnP5m0bt26dXVv7032T6NZO/32HsWQ+vPDj2xIIfW5n0ynf033I5uc3HvvvfHss89GRMS6detqL6ybaPbs2XHxxRfXrjdt2jRp3ZYtW6Jarbb0z/XXX5/5viALWX5+HH744RER8Z//+Z+T9hh9/tBDDx334xs42LL+vjDTCCQ5+fGPf1x7fMYZZ9Stff/73z/pOpipsvz8OOGEEyIi4uGHH550/ehbxx9//PGtjgmZ8n2hPoEkJ7/4xS9qj0866aS6tUuWLIlly5ZFRMTOnTtj165duc4GqWX5+bFmzZo49NBDY/v27fFv//ZvB6z//ve/HxER69evn+7YMC2+L9QnkOTk8ccfrz1euXJlw/qxNWPXwkyU5efHnDlz4pJLLomIiIsuumjc+zds2rQpNm/eHAsXLoxPfOIT05wapsf3hfr8lk1OXnjhhdrjhQsXNqx//etfP+naLP3oRz+KK6+8sna9e/fuiNj/2zhz586NiP1vs/25z30ul38/jMr68+Ov/uqv4u67744tW7bEkUceGevWrYvf/OY3cd9998WrXvWquOGGG/wdG5LL+tzPtK/pAklORt8bISJi3rx5Devnz59fe5zXeyXs2rUr/vVf//WA50ff6TIi4uijj87l3w1jZf358epXvzp+8pOfxN/+7d/Gd7/73bjllluiXC7H+vXr44orrpjyxYNwMGV97mfa13SBpIucf/75bb/FMRTdq1/96rjsssvisssuSz0KHBQz7Wu615DkZOyvF+7du7dh/Ysvvlh77NYyM53PD7qRc1+fQJKT1772tbXHg4ODDeufe+65SdfCTOTzg27k3NcnkOTkqKOOqj3evn17w/qxNWPXwkzk84Nu5NzXJ5Dk5Ljjjqs9fuihh+rW7ty5M5555pmIiFi0aFEcdthhuc4Gqfn8oBs59/UJJDl53/veV3vc6F32Nm/eXHvc6N37YCbw+UE3cu7rE0hysm7duliyZElE7P87NKNvXz3Rvn374qtf/Wrt+txzzz0o80FKPj/oRs59fQJJTmbPnh1XXHFF7fq8886L3/72twfUffazn639zviaNWvi9NNPP1gjQjI+P+hGzn19pWq1Wk09RJFs3749vvWtb417btu2bXHrrbdGRMSqVavigx/84LiPn3rqqXHqqace0Ovll1+OM844I+68886I2P+3CS644II45phjYvfu3bFp06YYGBiIiP2voB4YGIhjjz02j21BJnx+0I2c+4Okyjg/+9nPqhHR0j+f//znp+w3PDxcPfPMM+uuf+Mb31i9//77D94moU0+P+hGzv3B4Uc2OVuwYEHceuut8cMf/jDOPvvsWLZsWcydOzcWLlwYb3/72+Oqq66Kxx57LE455ZTUo8JB5/ODbuTcT86PbACA5NwhAQCSE0gAgOQEEgAgOYEEAEhOIAEAkhNIAIDkBBIAIDmBBABITiABAJITSACA5AQSACA5gQQASE4gAQCSE0gAgOQEEgAgOYEEAEju/wPLeLkYd9QkKwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Assuming df_melted and df are already created as in the previous code\n",
+    "\n",
+    "# Print unique dataset names in both DataFrames\n",
+    "print(\"Datasets in df_melted:\", df_melted['Dataset'].unique())\n",
+    "print(\"Datasets in batch_8_df:\", batch_8_df['Dataset'].unique())\n",
+    "\n",
+    "# Filter the data for batch size 8\n",
+    "batch_8_df = df[df['BatchSize'] == 8]\n",
+    "\n",
+    "# Get unique datasets\n",
+    "datasets = batch_8_df['Dataset'].unique()\n",
+    "\n",
+    "# Create a mapping between dataset names if necessary\n",
+    "dataset_mapping = {\n",
+    "    'berkeley_cable_routing': 'Cable Routing',\n",
+    "    'nyu_door_opening_surprising_effectiveness': 'Door Opening',\n",
+    "    'bridge': 'Bridge'\n",
+    "    # Add more mappings if needed\n",
+    "}\n",
+    "\n",
+    "# use the same color for the same format\n",
+    "color_mapping = {\n",
+    "    'RLDS': 'purple',\n",
+    "    'HDF5': 'green',\n",
+    "    'Fog-VLA-DM-lossless': 'orange',\n",
+    "    'LEROBOT': 'red',\n",
+    "    'Fog-VLA-DM': 'blue'\n",
+    "}\n",
+    "\n",
+    "# Create a figure for each dataset\n",
+    "for dataset in datasets:\n",
+    "    plt.figure(figsize=(6, 6))\n",
+    "    \n",
+    "    dataset_df = batch_8_df[batch_8_df['Dataset'] == dataset]\n",
+    "    \n",
+    "    # Map the dataset name if necessary\n",
+    "    mapped_dataset = dataset_mapping.get(dataset, dataset)\n",
+    "    \n",
+    "    # Create the scatter plot\n",
+    "    for format, (color, marker) in format_styles.items():\n",
+    "        data = dataset_df[dataset_df['Format'] == format]\n",
+    "        try:\n",
+    "            size = df_melted[(df_melted['Dataset'] == mapped_dataset) & (df_melted['Format'] == format)]['Size (GB)'].values[0]\n",
+    "            plt.scatter(size * 0.02, data['LoadingTime(s)'], \n",
+    "                        color=color_mapping[format], marker=marker, label=format, s=100)\n",
+    "        except IndexError:\n",
+    "            print(f\"Warning: No data found for dataset '{mapped_dataset}' and format '{format}'\")\n",
+    "            continue\n",
+    "\n",
+    "    # Customize the plot\n",
+    "    # plt.xlabel('Dataset Size (GB)')\n",
+    "    # plt.ylabel('Throughput (episodes/s)')\n",
+    "    # plt.title(f'{mapped_dataset} - Dataset Size vs Loading Time (Batch Size 8)')\n",
+    "    # plt.legend()\n",
+    "    \n",
+    "    \n",
+    "    plt.xscale('log')\n",
+    "    plt.yscale('log')\n",
+    "    \n",
+    "    plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
+    "\n",
+    "    if mapped_dataset == 'Door Opening':\n",
+    "        plt.ylim(100, 1500)\n",
+    "    # Show the plot\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(f'./{mapped_dataset}_size_vs_cost_overall.pdf')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9a655a70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3200483/808706995.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          mean    median       min        max\n",
+      "Format                                                       \n",
+      "Fog-VLA-DM-lossless   0.785081  0.696694  0.288564   1.400209\n",
+      "H264                  0.733893  0.734583  0.370579   1.119697\n",
+      "HDF5                  0.477196  0.474345  0.220551   0.736611\n",
+      "LEROBOT              11.711865  4.944148  1.413318  34.672886\n",
+      "RLDS                  9.262323  4.681807  0.403119  44.951988\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df = pd.read_csv('./format_comparison_results.csv')\n",
+    "\n",
+    "# Update the format names\n",
+    "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n",
+    "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n",
+    "\n",
+    "# Calculate speedup factors\n",
+    "def calculate_speedup(group):\n",
+    "    fog_vla_dm_time = group[group['Format'] == 'Fog-VLA-DM']['AverageLoadingTime(s)'].values[0]\n",
+    "    group['SpeedupFactor'] = group['AverageLoadingTime(s)'] / fog_vla_dm_time\n",
+    "    return group\n",
+    "\n",
+    "df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n",
+    "\n",
+    "# Set up the plot\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "\n",
+    "# Create the box plot\n",
+    "sns.boxplot(x='Format', y='SpeedupFactor', data=df[df['Format'] != 'Fog-VLA-DM'])\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Latency Speedup Factor of Fog-VLA-DM Compared to Alternatives')\n",
+    "plt.xlabel('Format')\n",
+    "plt.ylabel('Speedup Factor (higher is better)')\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "# Add a horizontal line at y=1 to represent Fog-VLA-DM\n",
+    "plt.axhline(y=1, color='r', linestyle='--', label='Fog-VLA-DM')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save the plot\n",
+    "plt.savefig('latency_speedup_comparison.pdf')\n",
+    "plt.show()\n",
+    "\n",
+    "# Print summary statistics\n",
+    "summary = df[df['Format'] != 'Fog-VLA-DM'].groupby('Format')['SpeedupFactor'].agg(['mean', 'median', 'min', 'max'])\n",
+    "print(summary)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/benchmarks/openx.py b/benchmarks/openx.py
new file mode 100644
index 0000000..f8db194
--- /dev/null
+++ b/benchmarks/openx.py
@@ -0,0 +1,441 @@
+import os
+import subprocess
+import argparse
+import time
+import numpy as np
+from fog_x.loader import RLDSLoader, VLALoader, HDF5Loader
+import tensorflow as tf
+import pandas as pd
+import fog_x
+import csv
+import stat
+from fog_x.loader.lerobot import LeRobotLoader
+from fog_x.loader.vla import get_vla_dataloader
+from fog_x.loader.hdf5 import get_hdf5_dataloader
+
+# Constants
+DEFAULT_EXP_DIR = "/mnt/data/fog_x/"
+DEFAULT_NUMBER_OF_TRAJECTORIES = -1  # Load all trajectories
+DEFAULT_DATASET_NAMES = [
+    "nyu_door_opening_surprising_effectiveness",
+    "berkeley_cable_routing",
+    "berkeley_autolab_ur5",
+    "bridge",
+]
+# DEFAULT_DATASET_NAMES = ["bridge"]
+# CACHE_DIR = "/tmp/fog_x/cache/"
+CACHE_DIR  = "/mnt/data/fog_x/cache/"
+DEFAULT_LOG_FREQUENCY = 20
+
+# suppress tensorflow warnings
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import logging
+logger = logging.getLogger(__name__)
+
+class DatasetHandler:
+    def __init__(
+        self,
+        exp_dir,
+        dataset_name,
+        num_batches,
+        dataset_type,
+        batch_size,
+        log_frequency=DEFAULT_LOG_FREQUENCY,
+    ):
+        self.exp_dir = exp_dir
+        self.dataset_name = dataset_name
+        self.num_batches = num_batches
+        self.dataset_type = dataset_type
+        self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name)
+        self.batch_size = batch_size
+        # Resolve the symbolic link if the dataset_dir is a soft link
+        self.dataset_dir = os.path.realpath(self.dataset_dir)
+        self.log_frequency = log_frequency
+        self.results = []
+        self.log_level = "debug"
+
+    def measure_average_trajectory_size(self):
+        """Calculates the average size of trajectory files in the dataset directory."""
+        total_size = 0
+        for dirpath, dirnames, filenames in os.walk(self.dataset_dir):
+            for f in filenames:
+                file_path = os.path.join(dirpath, f)
+                total_size += os.path.getsize(file_path)
+        
+        logger.debug(f"total_size: {total_size} of directory {self.dataset_dir}")
+        # trajectory number 
+        traj_num = 0
+        if self.dataset_name == "nyu_door_opening_surprising_effectiveness":
+            traj_num = 435
+        if self.dataset_name == "berkeley_cable_routing":
+            traj_num = 1482
+        if self.dataset_name == "bridge":
+            traj_num = 25460
+        if self.dataset_name == "berkeley_autolab_ur5":
+            traj_num = 896
+        return (total_size / traj_num) / (1024 * 1024)  # Convert to MB
+
+    def clear_cache(self):
+        """Clears the cache directory."""
+        if os.path.exists(CACHE_DIR):
+            logger.info(f"Clearing cache directory: {CACHE_DIR}")
+            subprocess.run(["rm", "-rf", CACHE_DIR], check=True)
+
+    def clear_os_cache(self):
+        """Clears the OS cache."""
+        subprocess.run(["sync"], check=True)
+        subprocess.run(["sudo", "sh", "-c", "echo 3 > /proc/sys/vm/drop_caches"], check=True)
+        logger.info(f"Cleared OS cache")
+        
+    def _recursively_load_data(self, data):
+        logger.debug(f"Data summary for loader {self.dataset_type.upper()}")
+        if None in data:
+            logger.warning(f"None value found in data")
+        def summarize_trajectory(trajectory):
+            def summarize_value(value):
+                if isinstance(value, np.ndarray):
+                    return value.shape
+                elif isinstance(value, (list, tuple)):
+                    if len(value) > 0 and isinstance(value[0], np.ndarray):
+                        return [v.shape for v in value]
+                    return len(value)
+                elif isinstance(value, dict):
+                    return {k: summarize_value(v) for k, v in value.items()}
+                elif isinstance(value, str):
+                    return value
+                else:
+                    logger.warning(f"Unknown type: {type(value)}")
+                    return type(value).__name__
+
+            return {key: summarize_value(value) for key, value in trajectory.items()}
+
+        trajectory_summaries = [summarize_trajectory(trajectory) for trajectory in data]
+
+        log_func = logger.debug if self.log_level == 'debug' else logger.info
+        for i, summary in enumerate(trajectory_summaries):
+            log_func(f"Trajectory {i + 1}:")
+            for feature, dimension in summary.items():
+                if isinstance(dimension, dict):
+                    log_func(f"  {feature}:")
+                    for sub_feature, sub_dimension in dimension.items():
+                        log_func(f"    {sub_feature}: {sub_dimension}")
+                else:
+                    log_func(f"  {feature}: {dimension}")
+
+        log_func(f"Total number of trajectories: {len(trajectory_summaries)}")
+
+    def write_result(self, format_name, elapsed_time, index):
+        result = {
+            "Dataset": self.dataset_name,
+            "Format": format_name,
+            "AverageTrajectorySize(MB)": self.measure_average_trajectory_size(),
+            "LoadingTime(s)": elapsed_time,
+            "AverageLoadingTime(s)": elapsed_time / (index + 1),
+            "Index": index,
+            "BatchSize": self.batch_size,
+        }
+
+        csv_file = f"{self.dataset_name}_results.csv"
+        file_exists = os.path.isfile(csv_file)
+
+        with open(csv_file, "a", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=result.keys())
+            if not file_exists:
+                writer.writeheader()
+            writer.writerow(result)
+
+    def measure_random_loading_time(self):
+        start_time = time.time()
+        loader = self.get_loader()
+        last_batch_time = time.time()
+        for batch_num, data in enumerate(loader):
+            if batch_num >= self.num_batches:
+                break
+            self._recursively_load_data(data)
+            current_batch_time = time.time()
+            elapsed_time = current_batch_time - last_batch_time
+            last_batch_time = current_batch_time
+
+            self.write_result(
+                f"{self.dataset_type.upper()}", elapsed_time, batch_num
+            )
+            if batch_num % self.log_frequency == 0:
+                logger.info(
+                    f"{self.dataset_type.upper()} - Loaded {batch_num} random {self.batch_size} batches from {self.dataset_name}, Time: {elapsed_time:.2f} s, Total Average Time: {(current_batch_time - start_time) / (batch_num + 1):.2f} s, Batch Average Time: {elapsed_time / self.batch_size:.2f} s"
+                )
+
+        return time.time() - start_time
+
+    def get_loader(self):
+        raise NotImplementedError("Subclasses must implement get_loader method")
+
+
+class RLDSHandler(DatasetHandler):
+    def __init__(
+        self,
+        exp_dir,
+        dataset_name,
+        num_batches,
+        batch_size,
+        log_frequency=DEFAULT_LOG_FREQUENCY,
+    ):
+        super().__init__(
+            exp_dir,
+            dataset_name,
+            num_batches,
+            dataset_type="rlds",
+            batch_size=batch_size,
+            log_frequency=log_frequency,
+        )
+        self.file_extension = ".tfrecord"
+
+    def get_loader(self):
+        return RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size)
+
+    def _recursively_load_data(self, data):
+        log_level = self.log_level
+        # rlds returns a list of dictionaries
+        log_func = logger.debug if log_level == 'debug' else logger.info
+        log_func(f"Data summary for loader {self.dataset_type.upper()}")
+        for i, trajectory in enumerate(data):
+            log_func(f"Trajectory {i + 1}:")
+            # each trajectory is a list of dictionaries
+            for j, step in enumerate(trajectory):
+                log_func(f"  Step {j + 1}:")
+                for key, value in step.items():
+                    if isinstance(value, np.ndarray):
+                        log_func(f"    {key}: {value.shape}")
+                    elif isinstance(value, dict):
+                        log_func(f"    {key}:")
+                        for sub_key, sub_value in value.items():
+                            log_func(f"      {sub_key}: {sub_value.shape}")
+                    else:
+                        log_func(f"    {key}: {type(value).__name__}")
+        log_func(f"Total number of trajectories: {len(data)}")
+
+class VLAHandler(DatasetHandler):
+    def __init__(
+        self,
+        exp_dir,
+        dataset_name,
+        num_batches,
+        batch_size,
+        log_frequency=DEFAULT_LOG_FREQUENCY,
+    ):
+        super().__init__(
+            exp_dir,
+            dataset_name,
+            num_batches,
+            dataset_type="vla",
+            batch_size=batch_size,
+            log_frequency=log_frequency,
+        )
+        self.file_extension = ".vla"
+
+    def get_loader(self):
+        return get_vla_dataloader(
+            self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR
+        )
+
+
+class HDF5Handler(DatasetHandler):
+    def __init__(
+        self,
+        exp_dir,
+        dataset_name,
+        num_batches,
+        batch_size,
+        log_frequency=DEFAULT_LOG_FREQUENCY,
+    ):
+        super().__init__(
+            exp_dir,
+            dataset_name,
+            num_batches,
+            dataset_type="hdf5",
+            batch_size=batch_size,
+            log_frequency=log_frequency,
+        )
+        self.file_extension = ".h5"
+
+    def get_loader(self):
+        return get_hdf5_dataloader(
+            path=os.path.join(self.dataset_dir, "*.h5"),
+            batch_size=self.batch_size,
+            num_workers=0,  # You can adjust this if needed
+        )
+
+
+class LeRobotHandler(DatasetHandler):
+    def __init__(
+        self,
+        exp_dir,
+        dataset_name,
+        num_batches,
+        batch_size,
+        log_frequency=DEFAULT_LOG_FREQUENCY,
+    ):
+        super().__init__(
+            exp_dir,
+            dataset_name,
+            num_batches,
+            dataset_type="hf",
+            batch_size=batch_size,
+            log_frequency=log_frequency,
+        )
+        self.file_extension = (
+            ""  # LeRobot datasets don't have a specific file extension
+        )
+
+    def get_loader(self):
+        path = os.path.join(self.exp_dir, "hf")
+        return LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size)
+
+    def _recursively_load_data(self, data):
+        import torch
+        log_level = self.log_level
+        # LeRobot returns a list of lists
+        log_func = logger.debug if log_level == 'debug' else logger.info
+        log_func(f"Data summary for loader {self.dataset_type.upper()}")
+        for i, trajectory in enumerate(data):
+            log_func(f"Trajectory {i + 1}:")
+            # each trajectory is a list of dictionaries
+            for j, step in enumerate(trajectory):
+                log_func(f"  Step {j + 1}:")
+                for key, value in step.items():
+                    if isinstance(value, np.ndarray):
+                        log_func(f"    {key}: {value.shape}")
+                    elif isinstance(value, dict):
+                        log_func(f"    {key}:")
+                        for sub_key, sub_value in value.items():
+                            log_func(f"      {sub_key}: {sub_value.shape}")
+                    elif isinstance(value, torch.Tensor):
+                        log_func(f"    {key}: {value.shape}")
+                    else:
+                        log_func(f"    {key}: {type(value).__name__}")
+        log_func(f"Total number of trajectories: {len(data)}")
+
+class FFV1Handler(DatasetHandler):
+    def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY):
+        super().__init__(exp_dir, dataset_name, num_batches, dataset_type="ffv1", batch_size=batch_size, log_frequency=log_frequency)
+        self.file_extension = ".vla"
+
+    def get_loader(self):
+        return VLALoader(self.dataset_dir, batch_size=self.batch_size)
+
+
+def evaluation(args):
+
+    csv_file = "format_comparison_results.csv"
+
+    if os.path.exists(csv_file):
+        existing_results = pd.read_csv(csv_file).to_dict("records")
+    else:
+        existing_results = []
+
+    new_results = []
+    for dataset_name in args.dataset_names:
+        logger.debug(f"Evaluating dataset: {dataset_name}")
+        
+        handlers = [
+            # VLAHandler(
+            #     args.exp_dir,
+            #     dataset_name,
+            #     args.num_batches,
+            #     args.batch_size,
+            #     args.log_frequency,
+            # ),
+            HDF5Handler(
+                args.exp_dir,
+                dataset_name,
+                args.num_batches,
+                args.batch_size,
+                args.log_frequency,
+            ),
+            # LeRobotHandler(
+            #     args.exp_dir,
+            #     dataset_name,
+            #     args.num_batches,
+            #     args.batch_size,
+            #     args.log_frequency,
+            # ),
+            # RLDSHandler(
+            #     args.exp_dir,
+            #     dataset_name,
+            #     args.num_batches,
+            #     args.batch_size,
+            #     args.log_frequency,
+            # ),
+            # FFV1Handler(
+            #     args.exp_dir,
+            #     dataset_name,
+            #     args.num_batches,
+            #     args.batch_size,
+            #     args.log_frequency,
+            # ),
+        ]
+
+        for handler in handlers:
+            handler.clear_cache()
+            handler.clear_os_cache()
+
+            avg_traj_size = handler.measure_average_trajectory_size()
+            random_load_time = handler.measure_random_loading_time()
+            new_results.append(
+                {
+                    "Dataset": dataset_name,
+                    "Format": f"{handler.dataset_type.upper()}",
+                    "AverageTrajectorySize(MB)": avg_traj_size,
+                    "LoadingTime(s)": random_load_time,
+                    "AverageLoadingTime(s)": random_load_time / (args.num_batches + 1),
+                    "Index": args.num_batches,
+                    "BatchSize": args.batch_size,
+                }
+            )
+            logger.debug(
+                f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s"
+            )
+
+        # Combine existing and new results
+        all_results = existing_results + new_results
+
+        # Write all results to CSV
+        results_df = pd.DataFrame(all_results)
+        results_df.to_csv(csv_file, index=False)
+        logger.debug(f"Results appended to {csv_file}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats."
+    )
+    parser.add_argument(
+        "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory."
+    )
+    parser.add_argument(
+        "--dataset_names",
+        nargs="+",
+        default=DEFAULT_DATASET_NAMES,
+        help="List of dataset names to evaluate.",
+    )
+
+    parser.add_argument(
+        "--log_frequency",
+        type=int,
+        default=DEFAULT_LOG_FREQUENCY,
+        help="Frequency of logging results.",
+    )
+    parser.add_argument(
+        "--num_batches",
+        type=int,
+        default=1000,
+        help="Number of batches to load for each loader.",
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=16, help="Batch size for loaders."
+    )
+    args = parser.parse_args()
+
+    evaluation(args)
diff --git a/evaluation.sh b/evaluation.sh
new file mode 100755
index 0000000..976ea22
--- /dev/null
+++ b/evaluation.sh
@@ -0,0 +1,22 @@
+# ask for sudo access
+sudo echo "Use sudo access for clearning cache"
+
+# Define a list of batch sizes to iterate through
+
+batch_sizes=(1 2 4 6 8 10 12 14 16)
+num_batches=200
+# batch_sizes=(1 2)
+
+# batch_sizes=(2)
+# num_batches=100
+
+# Iterate through each batch size
+for batch_size in "${batch_sizes[@]}"
+do
+    echo "Running benchmarks with batch size: $batch_size"
+    
+    # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size
+    python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size
+    # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size
+    # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size
+done
\ No newline at end of file
diff --git a/examples/Fog_X_Analytics_Demo.ipynb b/examples/Fog_X_Analytics_Demo.ipynb
deleted file mode 100644
index 2bd29ba..0000000
--- a/examples/Fog_X_Analytics_Demo.ipynb
+++ /dev/null
@@ -1,1019 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "99458164",
-   "metadata": {},
-   "source": [
-    "# Fog-X Demo\n",
-    "\n",
-    "In this demo, we show how to use Fog-X to collect and manage your robotics learning dataset. We show the following aspects of the Fog-X: \n",
-    "* Support for existing Open-X datasets\n",
-    "* Data Analytics and Management \n",
-    "* Use for Pytorch Learning\n",
-    "* Export and Share with Open-X (Tensorflow rlds) and HuggingFace\n",
-    "\n",
-    "We also compare the disk saving (43\\%!) of Fog-X at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "36ed049c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import fog_x \n",
-    "\n",
-    "dataset = fog_x.dataset.Dataset(\n",
-    "    name=\"demo_ds\",\n",
-    "    path=\"~/test_dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b636dea1",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ca883c1",
-   "metadata": {},
-   "source": [
-    "## Loading From Existing Open-X/RT-X datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f52d6801",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.load_rtx_episodes(\n",
-    "    name=\"berkeley_autolab_ur5\",\n",
-    "    split=\"train[:10]\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff7c5aa1",
-   "metadata": {},
-   "source": [
-    "### Trajectory Metadata and Data\n",
-    "\n",
-    "Fog-X makes a distinction between trajectory metadata and the actual data. \n",
-    "* **Metadata**: information that is consistent across a certain trajectory, such as language command, tags\n",
-    "* **Data**: data for individual steps within a trajectory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5f3c6241",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (11, 44)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>Finished</th><th>feature_gripper_closedness_action_type</th><th>feature_gripper_closedness_action_shape</th><th>gripper_closedness_action_count</th><th>feature_rotation_delta_type</th><th>feature_rotation_delta_shape</th><th>rotation_delta_count</th><th>feature_terminate_episode_type</th><th>feature_terminate_episode_shape</th><th>terminate_episode_count</th><th>feature_world_vector_type</th><th>feature_world_vector_shape</th><th>world_vector_count</th><th>feature_is_first_type</th><th>feature_is_first_shape</th><th>is_first_count</th><th>feature_is_last_type</th><th>feature_is_last_shape</th><th>is_last_count</th><th>feature_is_terminal_type</th><th>feature_is_terminal_shape</th><th>is_terminal_count</th><th>feature_hand_image_type</th><th>feature_hand_image_shape</th><th>hand_image_count</th><th>feature_image_type</th><th>feature_image_shape</th><th>image_count</th><th>feature_image_with_depth_type</th><th>feature_image_with_depth_shape</th><th>image_with_depth_count</th><th>feature_natural_language_embedding_type</th><th>feature_natural_language_embedding_shape</th><th>natural_language_embedding_count</th><th>feature_natural_language_instruction_type</th><th>feature_natural_language_instruction_shape</th><th>natural_language_instruction_count</th><th>feature_robot_state_type</th><th>feature_robot_state_shape</th><th>robot_state_count</th><th>feature_reward_type</th><th>feature_reward_shape</th><th>reward_count</th></tr><tr><td>i64</td><td>bool</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>0</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>71.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>71.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td></tr><tr><td>1</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>71.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>71.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>71.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>71.0</td></tr><tr><td>2</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>76.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>76.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>76.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>76.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>76.0</td></tr><tr><td>3</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>81.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>81.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>81.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>81.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>81.0</td></tr><tr><td>4</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>80.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>80.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>80.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>80.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>80.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>6</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>103.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>103.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>103.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>103.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>103.0</td></tr><tr><td>7</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>110.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>110.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>110.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>110.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>110.0</td></tr><tr><td>8</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>118.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>118.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>118.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>118.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>118.0</td></tr><tr><td>9</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>84.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>84.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>84.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>84.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>84.0</td></tr><tr><td>10</td><td>true</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;(3,)&quot;</td><td>97.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;bool&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>97.0</td><td>&quot;uint8&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;(480, 640, 1)&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;(512,)&quot;</td><td>97.0</td><td>&quot;string&quot;</td><td>&quot;()&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;(15,)&quot;</td><td>97.0</td><td>&quot;float32&quot;</td><td>&quot;()&quot;</td><td>97.0</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (11, 44)\n",
-       "┌───────────┬──────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-       "│ episode_i ┆ Finished ┆ feature_g ┆ feature_g ┆ … ┆ robot_sta ┆ feature_r ┆ feature_r ┆ reward_co │\n",
-       "│ d         ┆ ---      ┆ ripper_cl ┆ ripper_cl ┆   ┆ te_count  ┆ eward_typ ┆ eward_sha ┆ unt       │\n",
-       "│ ---       ┆ bool     ┆ osedness_ ┆ osedness_ ┆   ┆ ---       ┆ e         ┆ pe        ┆ ---       │\n",
-       "│ i64       ┆          ┆ actio…    ┆ actio…    ┆   ┆ f64       ┆ ---       ┆ ---       ┆ f64       │\n",
-       "│           ┆          ┆ ---       ┆ ---       ┆   ┆           ┆ str       ┆ str       ┆           │\n",
-       "│           ┆          ┆ str       ┆ str       ┆   ┆           ┆           ┆           ┆           │\n",
-       "╞═══════════╪══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-       "│ 0         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 71.0      ┆ float32   ┆ ()        ┆ 71.0      │\n",
-       "│ 1         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 71.0      ┆ float32   ┆ ()        ┆ 71.0      │\n",
-       "│ 2         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 76.0      ┆ float32   ┆ ()        ┆ 76.0      │\n",
-       "│ 3         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 81.0      ┆ float32   ┆ ()        ┆ 81.0      │\n",
-       "│ 4         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 80.0      ┆ float32   ┆ ()        ┆ 80.0      │\n",
-       "│ …         ┆ …        ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
-       "│ 6         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 103.0     ┆ float32   ┆ ()        ┆ 103.0     │\n",
-       "│ 7         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 110.0     ┆ float32   ┆ ()        ┆ 110.0     │\n",
-       "│ 8         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 118.0     ┆ float32   ┆ ()        ┆ 118.0     │\n",
-       "│ 9         ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 84.0      ┆ float32   ┆ ()        ┆ 84.0      │\n",
-       "│ 10        ┆ true     ┆ float32   ┆ ()        ┆ … ┆ 97.0      ┆ float32   ┆ ()        ┆ 97.0      │\n",
-       "└───────────┴──────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# metadata\n",
-    "trajectory_metadata = dataset.get_episode_info()\n",
-    "trajectory_metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d965ed5a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
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-       "}\n",
-       "</style>\n",
-       "<small>shape: (9, 17)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>episode_id</th><th>Timestamp</th><th>gripper_closedness_action</th><th>rotation_delta</th><th>terminate_episode</th><th>world_vector</th><th>is_first</th><th>is_last</th><th>is_terminal</th><th>hand_image</th><th>image</th><th>image_with_depth</th><th>natural_language_embedding</th><th>natural_language_instruction</th><th>robot_state</th><th>reward</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>str</td><td>f64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>1014.0</td><td>1014.0</td><td>1014.0</td><td>&quot;1014&quot;</td><td>1014.0</td><td>&quot;1014&quot;</td><td>1014.0</td><td>1014.0</td><td>1014.0</td><td>&quot;1014&quot;</td><td>&quot;1014&quot;</td><td>&quot;1014&quot;</td><td>&quot;1014&quot;</td><td>&quot;1014&quot;</td><td>&quot;1014&quot;</td><td>1014.0</td></tr><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;0&quot;</td><td>0.0</td><td>&quot;0&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>5.383629</td><td>1.7127e18</td><td>0.0</td><td>null</td><td>0.021696</td><td>null</td><td>0.010848</td><td>0.021696</td><td>0.021696</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0.010848</td></tr><tr><td>&quot;std&quot;</td><td>3.017515</td><td>1.3023e11</td><td>0.108839</td><td>null</td><td>0.145762</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0.103639</td></tr><tr><td>&quot;min&quot;</td><td>0.0</td><td>1.7127e18</td><td>-1.0</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>0.0</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;pick up the …</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>0.0</td></tr><tr><td>&quot;25%&quot;</td><td>3.0</td><td>1.7127e18</td><td>0.0</td><td>null</td><td>0.0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0.0</td></tr><tr><td>&quot;50%&quot;</td><td>6.0</td><td>1.7127e18</td><td>0.0</td><td>null</td><td>0.0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0.0</td></tr><tr><td>&quot;75%&quot;</td><td>8.0</td><td>1.7127e18</td><td>0.0</td><td>null</td><td>0.0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0.0</td></tr><tr><td>&quot;max&quot;</td><td>10.0</td><td>1.7127e18</td><td>1.0</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>1.0</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>1.0</td><td>1.0</td><td>1.0</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;\\x93NUMPY\\x0…</td><td>&quot;b&#x27;sweep the gr…</td><td>&quot;b&quot;\\x93NUMPY\\x0…</td><td>1.0</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (9, 17)\n",
-       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-       "│ statistic ┆ episode_i ┆ Timestamp ┆ gripper_c ┆ … ┆ natural_l ┆ natural_l ┆ robot_sta ┆ reward   │\n",
-       "│ ---       ┆ d         ┆ ---       ┆ losedness ┆   ┆ anguage_e ┆ anguage_i ┆ te        ┆ ---      │\n",
-       "│ str       ┆ ---       ┆ f64       ┆ _action   ┆   ┆ mbedding  ┆ nstructio ┆ ---       ┆ f64      │\n",
-       "│           ┆ f64       ┆           ┆ ---       ┆   ┆ ---       ┆ n         ┆ str       ┆          │\n",
-       "│           ┆           ┆           ┆ f64       ┆   ┆ str       ┆ ---       ┆           ┆          │\n",
-       "│           ┆           ┆           ┆           ┆   ┆           ┆ str       ┆           ┆          │\n",
-       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-       "│ count     ┆ 1014.0    ┆ 1014.0    ┆ 1014.0    ┆ … ┆ 1014      ┆ 1014      ┆ 1014      ┆ 1014.0   │\n",
-       "│ null_coun ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0.0      │\n",
-       "│ t         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       "│ mean      ┆ 5.383629  ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.010848 │\n",
-       "│ std       ┆ 3.017515  ┆ 1.3023e11 ┆ 0.108839  ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.103639 │\n",
-       "│ min       ┆ 0.0       ┆ 1.7127e18 ┆ -1.0      ┆ … ┆ b'\\x93NUM ┆ b'pick up ┆ b\"\\x93NUM ┆ 0.0      │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ PY\\x01\\x0 ┆ the blue  ┆ PY\\x01\\x0 ┆          │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ 0v\\x00{\\' ┆ cup and   ┆ 0v\\x00{'d ┆          │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ descr…    ┆ put i…    ┆ escr'…    ┆          │\n",
-       "│ 25%       ┆ 3.0       ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       "│ 50%       ┆ 6.0       ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       "│ 75%       ┆ 8.0       ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       "│ max       ┆ 10.0      ┆ 1.7127e18 ┆ 1.0       ┆ … ┆ b'\\x93NUM ┆ b'sweep   ┆ b\"\\x93NUM ┆ 1.0      │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ PY\\x01\\x0 ┆ the green ┆ PY\\x01\\x0 ┆          │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ 0v\\x00{\\' ┆ cloth to  ┆ 0v\\x00{'d ┆          │\n",
-       "│           ┆           ┆           ┆           ┆   ┆ descr…    ┆ the l…    ┆ escr'…    ┆          │\n",
-       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# data for ALL trajectories \n",
-    "# these data are loaded lazily that only actively used data is loaded to memory\n",
-    "all_step_data = dataset.get_step_data()\n",
-    "# use .describe to get the summary of the information\n",
-    "all_step_data.describe() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e065eeda",
-   "metadata": {},
-   "source": [
-    "### Lazy Loading Step Data\n",
-    "Al the step data are loaded on demand to save space in memory. You can see the loading time difference between the lazy loading and loading all the data from disk. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "46dfe5a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.2 µs ± 368 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# data for individual episode \n",
-    "%timeit dataset.get_step_data_by_episode_ids([1,2,3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d5d265ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.48 s ± 291 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit dataset.get_step_data_by_episode_ids([1,2,3], as_lazy_frame=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "443a9043",
-   "metadata": {},
-   "source": [
-    "## Data Analytics and Management\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c771c5e9",
-   "metadata": {},
-   "source": [
-    "### Example 1: Add new Episode information metadata and Filter\n",
-    "\n",
-    "Suppose another person collects another set of the data and you want to distinguish who collects what.  \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a7b97900",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-04-10 05:59:42.147783: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
-      "2024-04-10 06:00:06.033397: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
-      "2024-04-10 06:00:08.650303: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
-     ]
-    }
-   ],
-   "source": [
-    "# this loads another 2 episodes \n",
-    "dataset.load_rtx_episodes(\n",
-    "    name=\"berkeley_autolab_ur5\",\n",
-    "    split=\"train[3:5]\",\n",
-    "    additional_metadata={\"collector\": \"User 2\", \"custom_tag\": \"Partition_2\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31157fa6",
-   "metadata": {},
-   "source": [
-    "now the metadata table looks like"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "87177338",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (13, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>collector</th><th>custom_tag</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>0</td><td>null</td><td>null</td></tr><tr><td>1</td><td>null</td><td>null</td></tr><tr><td>2</td><td>null</td><td>null</td></tr><tr><td>3</td><td>null</td><td>null</td></tr><tr><td>4</td><td>null</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>8</td><td>null</td><td>null</td></tr><tr><td>9</td><td>null</td><td>null</td></tr><tr><td>10</td><td>null</td><td>null</td></tr><tr><td>11</td><td>&quot;User 2&quot;</td><td>&quot;Partition_2&quot;</td></tr><tr><td>12</td><td>&quot;User 2&quot;</td><td>&quot;Partition_2&quot;</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (13, 3)\n",
-       "┌────────────┬───────────┬─────────────┐\n",
-       "│ episode_id ┆ collector ┆ custom_tag  │\n",
-       "│ ---        ┆ ---       ┆ ---         │\n",
-       "│ i64        ┆ str       ┆ str         │\n",
-       "╞════════════╪═══════════╪═════════════╡\n",
-       "│ 0          ┆ null      ┆ null        │\n",
-       "│ 1          ┆ null      ┆ null        │\n",
-       "│ 2          ┆ null      ┆ null        │\n",
-       "│ 3          ┆ null      ┆ null        │\n",
-       "│ 4          ┆ null      ┆ null        │\n",
-       "│ …          ┆ …         ┆ …           │\n",
-       "│ 8          ┆ null      ┆ null        │\n",
-       "│ 9          ┆ null      ┆ null        │\n",
-       "│ 10         ┆ null      ┆ null        │\n",
-       "│ 11         ┆ User 2    ┆ Partition_2 │\n",
-       "│ 12         ┆ User 2    ┆ Partition_2 │\n",
-       "└────────────┴───────────┴─────────────┘"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset.get_episode_info().select([\"episode_id\", \"collector\", \"custom_tag\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "857f3c87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "episode_info = dataset.get_episode_info()\n",
-    "# querying non-existent metadata \n",
-    "metadata = episode_info.filter(episode_info[\"collector\"] == \"User_Do_No_Exist\")\n",
-    "episodes = dataset.read_by(metadata)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d713a974",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([<LazyFrame [16 cols, {\"episode_id\": Int64 … \"reward\": Float32}] at 0x7F9D34D62350>,\n",
-       "  <LazyFrame [16 cols, {\"episode_id\": Int64 … \"reward\": Float32}] at 0x7F9CA2F0CC70>],\n",
-       " shape: (9, 17)\n",
-       " ┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-       " │ statistic ┆ episode_i ┆ Timestamp ┆ gripper_c ┆ … ┆ natural_l ┆ natural_l ┆ robot_sta ┆ reward   │\n",
-       " │ ---       ┆ d         ┆ ---       ┆ losedness ┆   ┆ anguage_e ┆ anguage_i ┆ te        ┆ ---      │\n",
-       " │ str       ┆ ---       ┆ f64       ┆ _action   ┆   ┆ mbedding  ┆ nstructio ┆ ---       ┆ f64      │\n",
-       " │           ┆ f64       ┆           ┆ ---       ┆   ┆ ---       ┆ n         ┆ str       ┆          │\n",
-       " │           ┆           ┆           ┆ f64       ┆   ┆ str       ┆ ---       ┆           ┆          │\n",
-       " │           ┆           ┆           ┆           ┆   ┆           ┆ str       ┆           ┆          │\n",
-       " ╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-       " │ count     ┆ 80.0      ┆ 80.0      ┆ 80.0      ┆ … ┆ 80        ┆ 80        ┆ 80        ┆ 80.0     │\n",
-       " │ null_coun ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0.0      │\n",
-       " │ t         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-       " │ mean      ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0125   │\n",
-       " │ std       ┆ 0.0       ┆ 3.8792e9  ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.111803 │\n",
-       " │ min       ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ b'\\x93NUM ┆ b'sweep   ┆ b\"\\x93NUM ┆ 0.0      │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ PY\\x01\\x0 ┆ the green ┆ PY\\x01\\x0 ┆          │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ 0v\\x00{\\' ┆ cloth to  ┆ 0v\\x00{'d ┆          │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ descr…    ┆ the l…    ┆ escr'…    ┆          │\n",
-       " │ 25%       ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       " │ 50%       ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       " │ 75%       ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ null      ┆ null      ┆ null      ┆ 0.0      │\n",
-       " │ max       ┆ 11.0      ┆ 1.7127e18 ┆ 0.0       ┆ … ┆ b'\\x93NUM ┆ b'sweep   ┆ b\"\\x93NUM ┆ 1.0      │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ PY\\x01\\x0 ┆ the green ┆ PY\\x01\\x0 ┆          │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ 0v\\x00{\\' ┆ cloth to  ┆ 0v\\x00{'d ┆          │\n",
-       " │           ┆           ┆           ┆           ┆   ┆ descr…    ┆ the l…    ┆ escr'…    ┆          │\n",
-       " └───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata = episode_info.filter(episode_info[\"custom_tag\"] == \"Partition_2\")\n",
-    "episodes = dataset.read_by(metadata)\n",
-    "episodes, episodes[0].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b575fec7",
-   "metadata": {},
-   "source": [
-    "### Example 2: Extracts and Searches natural language instructions from step data \n",
-    "\n",
-    "Existing Open-X datasets store natural language instructions for every step, which costs inefficiency and manage complexity. This example shows \n",
-    "1. how to extracts natural language instruction from existing Open-X datasets\n",
-    "2. search for keywords or **regex** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "23a47f3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (3, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>natural_language_instruction</th></tr><tr><td>i64</td><td>binary</td></tr></thead><tbody><tr><td>0</td><td>b&quot;sweep\\x20the\\x20green\\x20cloth\\x20to\\x20the\\x20left\\x20side\\x20of\\x20the\\x20table&quot;</td></tr><tr><td>10</td><td>b&quot;put\\x20the\\x20ranch\\x20bottle\\x20into\\x20the\\x20pot&quot;</td></tr><tr><td>12</td><td>b&quot;pick\\x20up\\x20the\\x20blue\\x20cup\\x20and\\x20put\\x20it\\x20into\\x20the\\x20brown\\x20cup.\\x20&quot;</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (3, 2)\n",
-       "┌────────────┬───────────────────────────────────┐\n",
-       "│ episode_id ┆ natural_language_instruction      │\n",
-       "│ ---        ┆ ---                               │\n",
-       "│ i64        ┆ binary                            │\n",
-       "╞════════════╪═══════════════════════════════════╡\n",
-       "│ 0          ┆ b\"sweep\\x20the\\x20green\\x20cloth… │\n",
-       "│ 10         ┆ b\"put\\x20the\\x20ranch\\x20bottle\\… │\n",
-       "│ 12         ┆ b\"pick\\x20up\\x20the\\x20blue\\x20c… │\n",
-       "└────────────┴───────────────────────────────────┘"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id_to_language_instruction = (\n",
-    "    dataset.get_step_data()\n",
-    "    .select(\"episode_id\", \"natural_language_instruction\")# only interested in episode id and language column\n",
-    "    .collect() # the frame is lazily evaluated at memory when we call collect() \n",
-    ")\n",
-    "\n",
-    "# print out unique natural_language_instructions \n",
-    "# https://docs.pola.rs/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html \n",
-    "id_to_language_instruction.unique(subset=[\"natural_language_instruction\"], maintain_order=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c248af4f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_step_data = dataset.get_step_data() # get lazy frame of the entire step-level dataset\n",
-    "id_to_language_instruction = (\n",
-    "    all_step_data\n",
-    "    .select(\"episode_id\", \"natural_language_instruction\") \n",
-    "    .group_by(\"episode_id\") # group by unqiue language ids, since language instruction is stored for every step\n",
-    "    .last()  # since instruction is same for all steps in an episode, we can just take the last one\n",
-    "    .collect() # the frame is lazily evaluated until we call collect() \n",
-    ")\n",
-    "\n",
-    "# join with the metadata \n",
-    "episode_metadata = dataset.get_episode_info().join(id_to_language_instruction, on=\"episode_id\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4978f740",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "shape: (6, 2)\n",
-      "┌────────────┬───────────────────────────────────┐\n",
-      "│ episode_id ┆ decoded                           │\n",
-      "│ ---        ┆ ---                               │\n",
-      "│ i64        ┆ str                               │\n",
-      "╞════════════╪═══════════════════════════════════╡\n",
-      "│ 9          ┆ sweep the green cloth to the lef… │\n",
-      "│ 4          ┆ sweep the green cloth to the lef… │\n",
-      "│ 1          ┆ sweep the green cloth to the lef… │\n",
-      "│ 2          ┆ sweep the green cloth to the lef… │\n",
-      "│ 0          ┆ sweep the green cloth to the lef… │\n",
-      "│ 11         ┆ sweep the green cloth to the lef… │\n",
-      "└────────────┴───────────────────────────────────┘\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_6756/232788706.py:3: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.\n",
-      "  episode_metadata = episode_metadata.with_columns(episode_metadata['natural_language_instruction'].map_elements(lambda x: x.decode('utf-8')).alias('decoded'))\n"
-     ]
-    }
-   ],
-   "source": [
-    "import polars as pl \n",
-    "# Decode byte strings to strings\n",
-    "episode_metadata = episode_metadata.with_columns(episode_metadata['natural_language_instruction'].map_elements(lambda x: x.decode('utf-8')).alias('decoded'))\n",
-    "\n",
-    "# Filter rows where 'string_col' contains \"example\"\n",
-    "result = episode_metadata.filter(\n",
-    "    pl.col(\"decoded\").str.contains(\"green|red\").alias(\"cloth\") # supports regex!\n",
-    ")\n",
-    "print(result.select([\"episode_id\", \"decoded\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc16dd8d",
-   "metadata": {},
-   "source": [
-    "We use polars as backend for data processing and management. This example demonstrates its capabaility and flexiblitiy. Please refer to https://docs.pola.rs/py-polars/html/reference/lazyframe/index.html all the available interfaces "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "851a95a5",
-   "metadata": {},
-   "source": [
-    "## Use, Export and Share"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e4ed6a6",
-   "metadata": {},
-   "source": [
-    "### Huggingface dataset "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "c7bb9c0d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e422d249b5c441bd9e85e7b128465982",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating train split: 0 examples [00:00, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hugging face dataset: DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['episode_id', 'Timestamp', 'gripper_closedness_action', 'rotation_delta', 'terminate_episode', 'world_vector', 'is_first', 'is_last', 'is_terminal', 'hand_image', 'image', 'image_with_depth', 'natural_language_embedding', 'natural_language_instruction', 'robot_state', 'reward'],\n",
-      "        num_rows: 1217\n",
-      "    })\n",
-      "})\n"
-     ]
-    }
-   ],
-   "source": [
-    "import datasets\n",
-    "\n",
-    "huggingface_ds = dataset.get_as_huggingface_dataset()\n",
-    "\n",
-    "print(f\"Hugging face dataset: {huggingface_ds}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd38e642",
-   "metadata": {},
-   "source": [
-    "### Pytorch Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "3c54437b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieving episode at index 0\n",
-      "Retrieving episode at index 1\n",
-      "[    episode_id            Timestamp  gripper_closedness_action  \\\n",
-      "0           11  1712728768601166160                        0.0   \n",
-      "1           11  1712728768839768104                        0.0   \n",
-      "2           11  1712728768983350023                        0.0   \n",
-      "3           11  1712728769119575319                        0.0   \n",
-      "4           11  1712728769256151909                        0.0   \n",
-      "..         ...                  ...                        ...   \n",
-      "75          11  1712728781218967667                        0.0   \n",
-      "76          11  1712728781437725750                        0.0   \n",
-      "77          11  1712728781613065131                        0.0   \n",
-      "78          11  1712728781822132558                        0.0   \n",
-      "79          11  1712728781969148910                        0.0   \n",
-      "\n",
-      "                                       rotation_delta  terminate_episode  \\\n",
-      "0   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "1   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "2   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...                0.0   \n",
-      "3   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "4   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "..                                                ...                ...   \n",
-      "75  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "76  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "77  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "78  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                1.0   \n",
-      "79  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                1.0   \n",
-      "\n",
-      "                                         world_vector  is_first  is_last  \\\n",
-      "0   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...      True    False   \n",
-      "1   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "2   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "3   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "4   b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "..                                                ...       ...      ...   \n",
-      "75  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "76  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "77  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "78  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False     True   \n",
-      "79  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False     True   \n",
-      "\n",
-      "    is_terminal                                         hand_image  \\\n",
-      "0         False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "1         False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "2         False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "3         False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "4         False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "..          ...                                                ...   \n",
-      "75        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "76        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "77        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "78         True  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "79         True  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "\n",
-      "                                                image  \\\n",
-      "0   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "1   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "2   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "3   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "4   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "..                                                ...   \n",
-      "75  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "76  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "77  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "78  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "79  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "\n",
-      "                                     image_with_depth  \\\n",
-      "0   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "1   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "2   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "3   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "4   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "..                                                ...   \n",
-      "75  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "76  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "77  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "78  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "79  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "\n",
-      "                           natural_language_embedding  \\\n",
-      "0   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "1   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "2   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "3   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "4   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "..                                                ...   \n",
-      "75  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "76  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "77  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "78  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "79  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "\n",
-      "                         natural_language_instruction  \\\n",
-      "0   b'sweep the green cloth to the left side of th...   \n",
-      "1   b'sweep the green cloth to the left side of th...   \n",
-      "2   b'sweep the green cloth to the left side of th...   \n",
-      "3   b'sweep the green cloth to the left side of th...   \n",
-      "4   b'sweep the green cloth to the left side of th...   \n",
-      "..                                                ...   \n",
-      "75  b'sweep the green cloth to the left side of th...   \n",
-      "76  b'sweep the green cloth to the left side of th...   \n",
-      "77  b'sweep the green cloth to the left side of th...   \n",
-      "78  b'sweep the green cloth to the left side of th...   \n",
-      "79  b'sweep the green cloth to the left side of th...   \n",
-      "\n",
-      "                                          robot_state  reward  \n",
-      "0   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "1   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "2   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "3   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "4   b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "..                                                ...     ...  \n",
-      "75  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "76  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "77  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "78  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "79  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     1.0  \n",
-      "\n",
-      "[80 rows x 16 columns],      episode_id            Timestamp  gripper_closedness_action  \\\n",
-      "0            12  1712728784013260746                        0.0   \n",
-      "1            12  1712728784178409334                        0.0   \n",
-      "2            12  1712728784364737770                        0.0   \n",
-      "3            12  1712728784525771621                        0.0   \n",
-      "4            12  1712728784712715579                        0.0   \n",
-      "..          ...                  ...                        ...   \n",
-      "118          12  1712728805038905965                        0.0   \n",
-      "119          12  1712728805205202342                        0.0   \n",
-      "120          12  1712728805373845588                        0.0   \n",
-      "121          12  1712728805561803986                        0.0   \n",
-      "122          12  1712728805787041023                        0.0   \n",
-      "\n",
-      "                                        rotation_delta  terminate_episode  \\\n",
-      "0    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "1    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "2    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "3    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "4    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "..                                                 ...                ...   \n",
-      "118  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "119  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "120  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                0.0   \n",
-      "121  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                1.0   \n",
-      "122  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...                1.0   \n",
-      "\n",
-      "                                          world_vector  is_first  is_last  \\\n",
-      "0    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...      True    False   \n",
-      "1    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "2    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "3    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "4    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "..                                                 ...       ...      ...   \n",
-      "118  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "119  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "120  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False    False   \n",
-      "121  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False     True   \n",
-      "122  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     False     True   \n",
-      "\n",
-      "     is_terminal                                         hand_image  \\\n",
-      "0          False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "1          False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "2          False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "3          False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "4          False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "..           ...                                                ...   \n",
-      "118        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "119        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "120        False  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "121         True  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "122         True  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "\n",
-      "                                                 image  \\\n",
-      "0    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "1    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "2    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "3    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "4    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "..                                                 ...   \n",
-      "118  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "119  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "120  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "121  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "122  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'|u1\\', \\...   \n",
-      "\n",
-      "                                      image_with_depth  \\\n",
-      "0    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "1    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "2    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "3    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "4    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "..                                                 ...   \n",
-      "118  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "119  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "120  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "121  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "122  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "\n",
-      "                            natural_language_embedding  \\\n",
-      "0    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "1    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "2    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "3    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "4    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "..                                                 ...   \n",
-      "118  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "119  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "120  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "121  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "122  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...   \n",
-      "\n",
-      "                          natural_language_instruction  \\\n",
-      "0    b'pick up the blue cup and put it into the bro...   \n",
-      "1    b'pick up the blue cup and put it into the bro...   \n",
-      "2    b'pick up the blue cup and put it into the bro...   \n",
-      "3    b'pick up the blue cup and put it into the bro...   \n",
-      "4    b'pick up the blue cup and put it into the bro...   \n",
-      "..                                                 ...   \n",
-      "118  b'pick up the blue cup and put it into the bro...   \n",
-      "119  b'pick up the blue cup and put it into the bro...   \n",
-      "120  b'pick up the blue cup and put it into the bro...   \n",
-      "121  b'pick up the blue cup and put it into the bro...   \n",
-      "122  b'pick up the blue cup and put it into the bro...   \n",
-      "\n",
-      "                                           robot_state  reward  \n",
-      "0    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "1    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "2    b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "3    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "4    b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "..                                                 ...     ...  \n",
-      "118  b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f4\\', \\...     0.0  \n",
-      "119  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "120  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "121  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     0.0  \n",
-      "122  b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f4', 'fort...     1.0  \n",
-      "\n",
-      "[123 rows x 16 columns]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch \n",
-    "\n",
-    "metadata = dataset.get_episode_info()\n",
-    "metadata = metadata.filter(metadata[\"collector\"] == \"User 2\")\n",
-    "pytorch_ds = dataset.pytorch_dataset_builder(\n",
-    "    metadata=metadata\n",
-    ")\n",
-    "\n",
-    "# get samples from the dataset\n",
-    "for data in torch.utils.data.DataLoader(\n",
-    "    pytorch_ds,\n",
-    "    batch_size=2,\n",
-    "    collate_fn=lambda x: x,\n",
-    "    sampler=torch.utils.data.RandomSampler(pytorch_ds),\n",
-    "):\n",
-    "    print(data)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd797c72",
-   "metadata": {},
-   "source": [
-    "### As Open-X dataset \n",
-    "In tensorflow rlds dataset format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f3872a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.export(format=\"open-x\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3ca5c448-076b-4eb9-9eb2-6ccf1fd03454",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dataset_info.json\t      demo_ds-train.tfrecord-00004\n",
-      "demo_ds-train.tfrecord-00000  demo_ds-train.tfrecord-00005\n",
-      "demo_ds-train.tfrecord-00001  demo_ds-train.tfrecord-00006\n",
-      "demo_ds-train.tfrecord-00002  features.json\n",
-      "demo_ds-train.tfrecord-00003\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls ~/test_dataset/export"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e4cf0b65",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"fileFormat\": \"tfrecord\",\n",
-      "  \"name\": \"demo_ds\",\n",
-      "  \"splits\": [\n",
-      "    {\n",
-      "      \"filepathTemplate\": \"{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_INDEX}\",\n",
-      "      \"name\": \"train\",\n",
-      "      \"numBytes\": \"2417903909\",\n",
-      "      \"shardLengths\": [\n",
-      "        \"1\",\n",
-      "        \"1\",\n",
-      "        \"1\",\n",
-      "        \"1\",\n",
-      "        \"1\",\n",
-      "        \"1\",\n",
-      "        \"1\"\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"version\": \"0.0.1\"\n",
-      "}"
-     ]
-    }
-   ],
-   "source": [
-    "!cat ~/test_dataset/export/dataset_info.json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff0d75fb-c4fc-49ed-ad45-76b703c699b9",
-   "metadata": {},
-   "source": [
-    "## Disk Comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "58f39c38-167a-469a-897e-0f1dd5fc21bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.3G\t/root/test_dataset/export/\n"
-     ]
-    }
-   ],
-   "source": [
-    "# file size of generated rlds \n",
-    "!du -sh ~/test_dataset/export/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "800e0b5c-7dd1-454c-83ed-3830614a7990",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.6G\t/root/test_dataset/demo_ds/\n"
-     ]
-    }
-   ],
-   "source": [
-    "# file size of Fog-X dataset\n",
-    "!du -sh ~/test_dataset/demo_ds/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41bd1973-f468-44f5-aeb3-ae41ee94353e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/Fog_X_Cloud_Demo.ipynb b/examples/Fog_X_Cloud_Demo.ipynb
deleted file mode 100644
index 117ed7f..0000000
--- a/examples/Fog_X_Cloud_Demo.ipynb
+++ /dev/null
@@ -1,609 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Fog-RTX Cloud Data Collection Demo\n",
-        "\n",
-        "FogRTX supports a wide range of cloud service providers. In this workbook, we show the support of AWS and google cloud.\n",
-        "\n",
-        "### AWS"
-      ],
-      "metadata": {
-        "id": "upgVMgpfdSCk"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ex7qQ3klf2_y"
-      },
-      "outputs": [],
-      "source": [
-        "! git clone https://github.com/BerkeleyAutomation/fog_x.git\n",
-        "! cd fog_x && git checkout cloud-demo-dev && pip install ."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Install required AWS dependency and configure with your aws credential"
-      ],
-      "metadata": {
-        "id": "TbCXBT0rdgR7"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "! curl \"https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip\" -o \"awscliv2.zip\"\n",
-        "! unzip -q awscliv2.zip\n",
-        "! sudo ./aws/install\n",
-        "! aws configure"
-      ],
-      "metadata": {
-        "id": "k4Q0PXALS0Ol"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# create an AWS bucket named fog-rtx-test-east-1 (anything you want)\n",
-        "!aws s3api create-bucket --bucket fog-rtx-test-east-2 --region us-east-1"
-      ],
-      "metadata": {
-        "id": "r-VhhYFBdqit",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "dc9c4b4e-baa9-4401-c4d6-eabed618d30d"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "{\n",
-            "    \"Location\": \"/fog-rtx-test-east-2\"\n",
-            "}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip3 install boto3"
-      ],
-      "metadata": {
-        "id": "TlSvbrw8deO1"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### Creating or Loading from existing datasets\n",
-        "\n",
-        "\n",
-        "Fog-RTX can load from the existing bucket, and add more to it!"
-      ],
-      "metadata": {
-        "id": "ZesCYo2udsei"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import fog_x\n",
-        "\n",
-        "dataset = fog_x.dataset.Dataset(\n",
-        "    name=\"demo_ds\",\n",
-        "    path='s3://fog-rtx-test-east-2',\n",
-        ")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Il5tj-i1f8bG",
-        "outputId": "73e922e2-a44d-4d2d-bda9-97c54b15c445"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n",
-            "INFO:botocore.credentials:Found credentials in shared credentials file: ~/.aws/credentials\n",
-            "INFO:fog_x.database.polars_connector:Table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "read from exisitng AWS stored dataset"
-      ],
-      "metadata": {
-        "id": "TArD9Frhd_U8"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset.get_episode_info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        },
-        "id": "oLMT8ioUd6Km",
-        "outputId": "c7510513-96b8-4942-abc6-84c0541c6759"
-      },
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "shape: (1, 8)\n",
-              "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n",
-              "│ ---        ┆ ---      ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n",
-              "│ i64        ┆ bool     ┆ iew_type   ┆ iew_shape  ┆ t          ┆ ton_type  ┆ ton_shape ┆ t         │\n",
-              "│            ┆          ┆ ---        ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│            ┆          ┆ str        ┆ str        ┆ f64        ┆ str       ┆ str       ┆ f64       │\n",
-              "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 0          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘"
-            ],
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (1, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>Finished</th><th>feature_arm_camera_view_type</th><th>feature_arm_camera_view_shape</th><th>arm_camera_view_count</th><th>feature_gripper_acton_type</th><th>feature_gripper_acton_shape</th><th>gripper_acton_count</th></tr><tr><td>i64</td><td>bool</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>0</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr></tbody></table></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### Adding new data to the dataset"
-      ],
-      "metadata": {
-        "id": "lcij8xiWui0P"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "\n",
-        "# create a new trajectory\n",
-        "episode = dataset.new_episode()\n",
-        "# collect step data for the episode\n",
-        "episode.add(feature = \"arm_camera_view\", value = np.random.rand(480, 640, 3))\n",
-        "episode.add(feature = \"gripper_acton\", value = np.random.rand(7))\n",
-        "# Automatically time-aligns and saves the trajectory\n",
-        "episode.close()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "akiVQqstdnWR",
-        "outputId": "a71f273a-025e-4102-cab5-6ecc398140ff"
-      },
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.db_manager:Closing the episode with metadata {}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset.get_episode_info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 161
-        },
-        "id": "uHZZnvAmeqqx",
-        "outputId": "a827585e-d5d0-4fd7-ce9c-51350e50de71"
-      },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "shape: (2, 8)\n",
-              "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n",
-              "│ ---        ┆ ---      ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n",
-              "│ i64        ┆ bool     ┆ iew_type   ┆ iew_shape  ┆ t          ┆ ton_type  ┆ ton_shape ┆ t         │\n",
-              "│            ┆          ┆ ---        ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│            ┆          ┆ str        ┆ str        ┆ f64        ┆ str       ┆ str       ┆ f64       │\n",
-              "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 0          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "│ 1          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘"
-            ],
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (2, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>Finished</th><th>feature_arm_camera_view_type</th><th>feature_arm_camera_view_shape</th><th>arm_camera_view_count</th><th>feature_gripper_acton_type</th><th>feature_gripper_acton_shape</th><th>gripper_acton_count</th></tr><tr><td>i64</td><td>bool</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>0</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr><tr><td>1</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr></tbody></table></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### Load Cloud Dataset at different place!\n",
-        "The data is automatically uploaded to the cloud!\n",
-        "We can create a different reader (you can run this on a different machine).\n",
-        "The data is automatically loaded and read!"
-      ],
-      "metadata": {
-        "id": "mUneci9XeHsE"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset2 = fog_x.dataset.Dataset(\n",
-        "    name=\"demo_ds\",\n",
-        "    path='s3://fog-rtx-test-east-2',\n",
-        ")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cQHIKeNAeSrY",
-        "outputId": "421fb7d5-9839-4ab7-c935-26025ba783d3"
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n",
-            "INFO:fog_x.database.polars_connector:Table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# metadata\n",
-        "trajectory_metadata = dataset2.get_episode_info()\n",
-        "trajectory_metadata"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 161
-        },
-        "id": "E4slMiSzf-se",
-        "outputId": "79b9813c-beac-4ad2-8c06-625e3d388754"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "shape: (2, 8)\n",
-              "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n",
-              "│ ---        ┆ ---      ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n",
-              "│ i64        ┆ bool     ┆ iew_type   ┆ iew_shape  ┆ t          ┆ ton_type  ┆ ton_shape ┆ t         │\n",
-              "│            ┆          ┆ ---        ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│            ┆          ┆ str        ┆ str        ┆ f64        ┆ str       ┆ str       ┆ f64       │\n",
-              "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 0          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "│ 1          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘"
-            ],
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (2, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>Finished</th><th>feature_arm_camera_view_type</th><th>feature_arm_camera_view_shape</th><th>arm_camera_view_count</th><th>feature_gripper_acton_type</th><th>feature_gripper_acton_shape</th><th>gripper_acton_count</th></tr><tr><td>i64</td><td>bool</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>0</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr><tr><td>1</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr></tbody></table></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Google Cloud Platform"
-      ],
-      "metadata": {
-        "id": "cB7QVbp6i-Mx"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "This can also be done on GCP!\n",
-        "\n",
-        "Register google cloud credentials\n",
-        "\n",
-        "Alternative in non-colab environment, run following command instead:\n",
-        "```\n",
-        "gcloud auth application-default login   --quiet --no-launch-browser\n",
-        "```\n"
-      ],
-      "metadata": {
-        "id": "8MIV3MZUjNta"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import auth\n",
-        "PROJECT_ID = \"canvas-rampart-342500\"\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ryd_To6LL3nX",
-        "outputId": "714ea38c-11d9-44fd-b8c4-5cb4ebd8b242"
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:google.colab.auth:Failure refreshing credentials: (\"Failed to retrieve http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/?recursive=true from the Google Compute Engine metadata service. Status: 404 Response:\\nb''\", <google.auth.transport.requests._Response object at 0x7e1292cdaad0>)\n",
-            "INFO:google.colab.auth:Failure refreshing credentials: (\"Failed to retrieve http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/?recursive=true from the Google Compute Engine metadata service. Status: 404 Response:\\nb''\", <google.auth.transport.requests._Response object at 0x7e1292dbbf70>)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "! gcloud storage buckets create gs://fog_rtx_test  --location=us-east1"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fYM3ExvGL3z7",
-        "outputId": "31c6bc57-4c3a-4b6f-b7ef-4132af7a926c"
-      },
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Creating gs://fog_rtx_test/...\n",
-            "\u001b[1;31mERROR:\u001b[0m (gcloud.storage.buckets.create) HTTPError 409: Your previous request to create the named bucket succeeded and you already own it.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset = fog_x.dataset.Dataset(\n",
-        "    name=\"demo_ds\",\n",
-        "    path='gs://fog_rtx_test/',\n",
-        ")"
-      ],
-      "metadata": {
-        "id": "pd94S4VlL32u",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "840c1668-983d-4320-f052-34ab77bb5930"
-      },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n",
-            "WARNING:fog_x.database.polars_connector:Failed to load table demo_ds from gs://fog_rtx_test/demo_ds.parquet.\n",
-            "ERROR:fog_x.database.polars_connector:Table demo_ds does not exist, available tables are dict_keys([]).\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "\n",
-        "# create a new trajectory\n",
-        "episode = dataset.new_episode()\n",
-        "# collect step data for the episode\n",
-        "episode.add(feature = \"arm_camera_view\", value = np.random.rand(480, 640, 3))\n",
-        "episode.add(feature = \"gripper_acton\", value = np.random.rand(7))\n",
-        "# Automatically time-aligns and saves the trajectory\n",
-        "episode.close()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Boc13CkhmQEs",
-        "outputId": "7aa83acf-ce3e-437b-975c-00df0cb999b0"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.db_manager:Closing the episode with metadata {'Finished': True, 'arm_camera_view_count': 0, 'gripper_acton_count': 0}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset2 = fog_x.dataset.Dataset(\n",
-        "    name=\"demo_ds\",\n",
-        "    path='gs://fog_rtx_test/',\n",
-        ")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "LtzsrO_BtvHB",
-        "outputId": "5c5c2bec-f769-4bc2-e185-638a42127af6"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n",
-            "INFO:fog_x.database.polars_connector:Table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset2.get_episode_info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        },
-        "id": "95utD8pRtxws",
-        "outputId": "0871ad47-d812-41fe-8cc6-67bbb77fe10e"
-      },
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "shape: (1, 8)\n",
-              "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n",
-              "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n",
-              "│ ---        ┆ ---      ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n",
-              "│ i64        ┆ bool     ┆ iew_type   ┆ iew_shape  ┆ t          ┆ ton_type  ┆ ton_shape ┆ t         │\n",
-              "│            ┆          ┆ ---        ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
-              "│            ┆          ┆ str        ┆ str        ┆ f64        ┆ str       ┆ str       ┆ f64       │\n",
-              "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n",
-              "│ 0          ┆ true     ┆ float64    ┆ (480, 640, ┆ 0.0        ┆ float64   ┆ (7,)      ┆ 0.0       │\n",
-              "│            ┆          ┆            ┆ 3)         ┆            ┆           ┆           ┆           │\n",
-              "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘"
-            ],
-            "text/html": [
-              "<div><style>\n",
-              ".dataframe > thead > tr,\n",
-              ".dataframe > tbody > tr {\n",
-              "  text-align: right;\n",
-              "  white-space: pre-wrap;\n",
-              "}\n",
-              "</style>\n",
-              "<small>shape: (1, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>episode_id</th><th>Finished</th><th>feature_arm_camera_view_type</th><th>feature_arm_camera_view_shape</th><th>arm_camera_view_count</th><th>feature_gripper_acton_type</th><th>feature_gripper_acton_shape</th><th>gripper_acton_count</th></tr><tr><td>i64</td><td>bool</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>0</td><td>true</td><td>&quot;float64&quot;</td><td>&quot;(480, 640, 3)&quot;</td><td>0.0</td><td>&quot;float64&quot;</td><td>&quot;(7,)&quot;</td><td>0.0</td></tr></tbody></table></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### Known issues\n",
-        "\n",
-        "1. `export` as rlds format to the cloud directly does not work yet for S3 (known issue for tensorflow Gfile)\n",
-        "2. (will fix) automatically check the existence"
-      ],
-      "metadata": {
-        "id": "P2RCUMs6knNc"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "QKS5jK-Qk9fN"
-      },
-      "execution_count": 14,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
diff --git a/examples/analytics/README.md b/examples/analytics/README.md
deleted file mode 100644
index b1bd4b4..0000000
--- a/examples/analytics/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Planned Data Analytics Examples 
-
-
-Since the episode metadata is dataframe that is very easy to work with, we demonstrate
-the capability with the following examples that work on the actual step data. 
-* **extract and group columns**: we extract natural language instruction from steps and use it to tag episodes (done)
-* **batch transformation**: we resize images. This involves creating a column, resizing images, adding a new column to store the images, and save the transformation
-* **tagging** This runs yolo on the first frame and save the tag to the metadata
-* **summary stats** aggregate a dataset-wise average of a matrix 
\ No newline at end of file
diff --git a/examples/analytics/dataset_organizer.py b/examples/analytics/dataset_organizer.py
deleted file mode 100644
index 222ddd8..0000000
--- a/examples/analytics/dataset_organizer.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import fog_x
-
-DATASETS = [
-    "fractal20220817_data",
-    "kuka",
-    "bridge",
-    "taco_play",
-    "jaco_play",
-    "berkeley_cable_routing",
-    "roboturk",
-    "nyu_door_opening_surprising_effectiveness",
-    "viola",
-    "berkeley_autolab_ur5",
-    "toto",
-    "columbia_cairlab_pusht_real",
-    "stanford_kuka_multimodal_dataset_converted_externally_to_rlds",
-    "nyu_rot_dataset_converted_externally_to_rlds",
-    "stanford_hydra_dataset_converted_externally_to_rlds",
-    "austin_buds_dataset_converted_externally_to_rlds",
-    "nyu_franka_play_dataset_converted_externally_to_rlds",
-    "maniskill_dataset_converted_externally_to_rlds",
-    "cmu_franka_exploration_dataset_converted_externally_to_rlds",
-    "ucsd_kitchen_dataset_converted_externally_to_rlds",
-    "ucsd_pick_and_place_dataset_converted_externally_to_rlds",
-    "austin_sailor_dataset_converted_externally_to_rlds",
-    "austin_sirius_dataset_converted_externally_to_rlds",
-    "bc_z",
-    "usc_cloth_sim_converted_externally_to_rlds",
-    "utokyo_pr2_opening_fridge_converted_externally_to_rlds",
-    "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds",
-    "utokyo_saytap_converted_externally_to_rlds",
-    "utokyo_xarm_pick_and_place_converted_externally_to_rlds",
-    "utokyo_xarm_bimanual_converted_externally_to_rlds",
-    "robo_net",
-    "berkeley_mvp_converted_externally_to_rlds",
-    "berkeley_rpt_converted_externally_to_rlds",
-    "kaist_nonprehensile_converted_externally_to_rlds",
-    "stanford_mask_vit_converted_externally_to_rlds",
-    "tokyo_u_lsmo_converted_externally_to_rlds",
-    "dlr_sara_pour_converted_externally_to_rlds",
-    "dlr_sara_grid_clamp_converted_externally_to_rlds",
-    "dlr_edan_shared_control_converted_externally_to_rlds",
-    "asu_table_top_converted_externally_to_rlds",
-    "stanford_robocook_converted_externally_to_rlds",
-    "eth_agent_affordances",
-    "imperialcollege_sawyer_wrist_cam",
-    "iamlab_cmu_pickup_insert_converted_externally_to_rlds",
-    "uiuc_d3field",
-    "utaustin_mutex",
-    "berkeley_fanuc_manipulation",
-    "cmu_play_fusion",
-    "cmu_stretch",
-    "berkeley_gnm_recon",
-    "berkeley_gnm_cory_hall",
-    # "berkeley_gnm_sac_son",
-]
-
-
-objects = ["NOTEXIST", "marker", "cloth", "cup", "object", "bottle", "block", "drawer", "lid", "mug"]
-tasks = ["NOTEXIST", "put", "move", "pick", "remove", "take", "open", "close", "place", "turn", "push", 
-         "insert", "stack", "lift", "pour"] # things not in DROID
-views = ["NOTEXIST", "wrist", "top", "other"]
-
-dataset_id = 0
-for dataset_name in DATASETS:
-    dataset = fog_x.dataset.Dataset(
-        name=dataset_name,
-        path="~/rtx_datasets",
-    )
-
-    dataset._prepare_rtx_metadata(
-        name=dataset_name,
-        sample_size = 100,
-        shuffle=True,
-    )
-
-for dataset_name in DATASETS:
-    dataset = fog_x.dataset.Dataset(
-        name=dataset_name,
-        path="~/rtx_datasets",
-    )
-    info = dataset.get_episode_info()
-
-    for episode_metadata in info.iter_rows(named = True):
-        instruction = episode_metadata["natural_language_instruction"]
-
-        d = dict()
-        instruction = instruction.lower().replace(",", "").replace("\n", "").replace("\"", "").replace("\'", "")
-        d["dataset_id"] = f"dataset-{dataset_id}"
-        d["info"] = instruction
-        task_id = -1 
-        for task in tasks:
-            if task in instruction:
-                task_id = tasks.index(task)
-        if task_id == -1:
-            task_id = len(tasks) - 1
-
-        obj_id = -1
-        for obj in objects:
-            if obj in instruction:
-                obj_id = objects.index(obj)
-        if obj_id == -1:
-            obj_id = len(objects) - 1
-            
-        d["task_id"] = f"task-{task_id}"
-        d["object_id"] = f"object-{obj_id}"
-
-        images_features = [col for col in info.columns if col.startswith("video_path_")]
-        for i, image_feature in enumerate(images_features):
-            path = episode_metadata[image_feature]
-            d["poster"] = f"videos/{dataset_name}_viz/{path}.jpg"
-            d["src"] = f"videos/{dataset_name}_viz/{path}.mp4"
-            view_id = -1
-            for view in views:
-                if view in path:
-                    view_id = views.index(view)
-            if view_id == -1:   
-                view_id = len(views) - 1
-            
-            d["view_id"] = f"view-{view_id}"
-
-            # print d in JSON format 
-            with open("/tmp/dataset_info.txt", "a") as file:
-                printable = str(d).replace("\'", "\"")
-                file.write(f'JSON.parse(\'{printable}\'),\n')
-
-            
-        # write as a line of JSON.parse('{"info": "Unfold the tea towel", "poster": "videos/bridge_viz/bridge_0_image.jpg", "src": "videos/bridge_viz/bridge_0_image.mp4"}'),
-        # print (f'JSON.parse(\'{{"info": "{instruction}", "poster": "videos/{dataset_name}_viz/{dataset_name}_{episode_id}_image.jpg", "src": "videos/{dataset_name}_viz/{dataset_name}_{dataset_id}_image.mp4"}}\'),')
-    dataset_id += 1
\ No newline at end of file
diff --git a/examples/analytics/extract_column.py b/examples/analytics/extract_column.py
deleted file mode 100644
index ef321d4..0000000
--- a/examples/analytics/extract_column.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import fog_x
-
-dataset = fog_x.dataset.Dataset(
-    name="demo_ds",
-    path="~/test_dataset",
-)
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[:5]",
-)
-
-all_step_data = dataset.get_step_data() # get lazy polars frame of the entire dataset
-id_to_language_instruction = (
-    all_step_data
-    .select("episode_id", "natural_language_instruction") # only interested in episode id and language column
-    .group_by("episode_id") # group by unqiue language ids, since language instruction is stored for every step
-    .last()  # since instruction is same for all steps in an episode, we can just take the last one
-    .collect() # the frame is lazily evaluated if we call collect() 
-)
-
-# join with the trajectory metadata 
-dataset.get_episode_info().join(id_to_language_instruction, on="episode_id")
diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py
deleted file mode 100644
index 00ebf7b..0000000
--- a/examples/basic/hello_world.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import fog_x
-
-# 🦊 Dataset Creation 
-# from distributed dataset storage 
-dataset = fog_x.Dataset(
-    name="demo_ds",
-    path="~/test_dataset", # can be AWS S3, Google Bucket! 
-)  
-
-# 🦊 Data collection: 
-# create a new trajectory
-episode = dataset.new_episode()
-# collect step data for the episode
-episode.add(feature = "arm_view", value = "image1.jpg")
-# Automatically time-aligns and saves the trajectory
-episode.close()
-
-# 🦊 Data Loading:
-# load from existing RT-X/Open-X datasets
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    additional_metadata={"collector": "User 2"}
-)
-
-# 🦊 Data Management and Analytics: 
-# Compute and memory efficient filter, map, aggregate, groupby
-episode_info = dataset.get_episode_info()
-desired_episodes = episode_info.filter(episode_info["collector"] == "User 2")
\ No newline at end of file
diff --git a/examples/basic/load.py b/examples/basic/load.py
deleted file mode 100644
index 0d96b87..0000000
--- a/examples/basic/load.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import polars as pl
-import pyarrow as pa
-import pyarrow.dataset as ds
-import pyarrow.parquet as pq
-
-import fog_x
-
-print(pl.scan_pyarrow_dataset(ds.dataset("~/test_dataset/steps")).collect())
diff --git a/examples/basic/main.py b/examples/basic/main.py
deleted file mode 100644
index 02156a6..0000000
--- a/examples/basic/main.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import fog_x
-
-# create a new dataset
-dataset = fog_x.dataset.Dataset(
-    name="test_rtx",
-    path="/tmp/rtx",
-    replace_existing=False,
-    db_connector=fog_x.database.PolarsConnector("/tmp/"),
-)
-
-for i in range(1, 10):
-    # create a new episode / trajectory
-    episode = dataset.new_episode(
-        metadata={
-            "collector_name": f"User #{i}",
-            "description": f"description #{i}",
-        }
-    )
-    # populate the episode with FeatureTypes
-    for j in range(1, 4):
-        episode.add(feature="feature_1", value=f"episode{i}_step{j}_feature_1")
-        episode.add(feature="feature_2", value=f"episode{i}_pose{j}_feature_2")
-    episode.close()
-
-# mark the current state as terminal state
-# and save the episode
-episode.close()
-
-# load the dataset
-metadata = dataset.get_metadata_as_pandas_df()
-# ...
-# do what you want like a typical pandas dataframe
-# Example: load with shuffled the episodes in the dataset
-# metadata = metadata.sample()
-# print(metadata)
-# episodes = dataset.read_by(metadata)
-# for episode in episodes:
-#     print(episode)
-
-# export the dataset
-# dataset.export("/tmp/rtx_export", format="rtx")
diff --git a/examples/data_collection_and_load.py b/examples/data_collection_and_load.py
new file mode 100644
index 0000000..975b24b
--- /dev/null
+++ b/examples/data_collection_and_load.py
@@ -0,0 +1,37 @@
+import fog_x
+import numpy as np
+import time 
+
+path = "/tmp/output.vla"
+
+# remove the existing file
+import os
+os.system(f"rm -rf {path}")
+os.system(f"rm -rf /tmp/*.cache")
+
+# 🦊 Data collection: 
+# create a new trajectory
+traj = fog_x.Trajectory(
+    path = path
+)
+
+# collect step data for the episode
+for i in range(100):
+    time.sleep(0.001)
+    traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8))
+    traj.add(feature = "gripper_pose", data = np.ones((4, 4), dtype=np.float32))
+    traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8))
+    traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8))
+    traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32))
+    traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32))
+    traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32))
+    traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32))
+    traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32))
+    traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32))
+
+traj.close()
+
+
+traj = fog_x.Trajectory(
+    path = path
+)
\ No newline at end of file
diff --git a/examples/dataloader/huggingface.py b/examples/dataloader/huggingface.py
deleted file mode 100644
index ca12a8c..0000000
--- a/examples/dataloader/huggingface.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import fog_x
-
-dataset = fog_x.dataset.Dataset(
-    name="demo_ds",
-    path="~/test_dataset",
-)
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[:1]",
-)
-
-huggingface_ds = dataset.get_as_huggingface_dataset()
-
-print(f"Hugging face dataset: {huggingface_ds}")
\ No newline at end of file
diff --git a/examples/dataloader/pytorch.py b/examples/dataloader/pytorch.py
deleted file mode 100644
index 95467d7..0000000
--- a/examples/dataloader/pytorch.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-
-import fog_x
-
-dataset = fog_x.dataset.Dataset(
-    name="demo_ds",
-    path="/tmp",
-)
-
-# dataset.load_rtx_episodes(
-#     name="berkeley_autolab_ur5",
-#     split="train[:2]",
-#     additional_metadata={"collector": "User 1"},
-# )
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[3:5]",
-    additional_metadata={"collector": "User 2"},
-)
-
-metadata = dataset.get_episode_info()
-metadata = metadata.filter(metadata["collector"] == "User 2")
-pytorch_ds = dataset.pytorch_dataset_builder(
-    metadata=metadata
-)
-
-# get samples from the dataset
-for data in torch.utils.data.DataLoader(
-    pytorch_ds,
-    batch_size=2,
-    collate_fn=lambda x: x,
-    sampler=torch.utils.data.RandomSampler(pytorch_ds),
-):
-    print(data)
diff --git a/examples/fixing_failed_conversions.py b/examples/fixing_failed_conversions.py
new file mode 100644
index 0000000..8401eb3
--- /dev/null
+++ b/examples/fixing_failed_conversions.py
@@ -0,0 +1,72 @@
+import argparse
+import os
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from fog_x.loader import RLDSLoader
+import fog_x
+import time
+def check_and_fix_conversion(file_path, data_traj, dataset_name, index, destination_dir, lossless):
+    try:
+        # Try to load the existing file
+        fog_x.Trajectory(file_path).load()
+        print(f"File {file_path} is valid.")
+        return index, True
+    except Exception as e:
+        print(f"Failed to load {file_path}. Attempting to fix: {e}")
+        
+        # If loading fails, attempt to reconvert
+        try:
+            data_traj = data_traj[0]
+            if lossless:
+                fog_x.Trajectory.from_list_of_dicts(
+                    data_traj, path=file_path,
+                    lossy_compression=False
+                )
+            else:
+                fog_x.Trajectory.from_list_of_dicts(
+                    data_traj, path=file_path, 
+                    lossy_compression=True,
+                )
+            print(f"Successfully fixed and reconverted data {index}")
+            return index, True
+        except Exception as e:
+            print(f"Failed to fix data {index}: {e}")
+            return index, False
+
+def main():
+    parser = argparse.ArgumentParser(description="Check and fix failed VLA conversions.")
+    parser.add_argument("--data_dir", required=True, help="Path to the original data directory")
+    parser.add_argument("--dataset_name", required=True, help="Name of the dataset")
+    parser.add_argument("--version", default="0.1.0", help="Dataset version")
+    parser.add_argument("--destination_dir", required=True, help="Directory containing converted files")
+    parser.add_argument("--split", default="train", help="Data split to use")
+    parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes")
+    parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format")
+
+    args = parser.parse_args()
+
+    loader = RLDSLoader(
+        path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling=False
+    )
+
+    with ProcessPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = []
+        for index, data_traj in enumerate(loader):
+            file_path = f"{args.destination_dir}/{args.dataset_name}/output_{index}.vla"
+            if os.path.exists(file_path):
+                future = executor.submit(check_and_fix_conversion, file_path, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)
+                futures.append(future)
+        
+        time.sleep(60)
+        failed_conversions = []
+        for future in as_completed(futures):
+            index, success = future.result()
+            if not success:
+                failed_conversions.append(index)
+
+    if failed_conversions:
+        print(f"Failed to fix {len(failed_conversions)} conversions: {failed_conversions}")
+    else:
+        print("All existing conversions are valid or have been successfully fixed.")
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/h5_loader.py b/examples/h5_loader.py
new file mode 100644
index 0000000..28c3b91
--- /dev/null
+++ b/examples/h5_loader.py
@@ -0,0 +1,21 @@
+from fog_x.loader.hdf5 import HDF5Loader
+import fog_x
+
+import os
+os.system("rm -rf /tmp/fog_x/*")
+
+loader = HDF5Loader("/home/kych/datasets/2024-07-03-red-on-cyan/**/trajectory_im128.h5")
+
+index = 0
+
+for data_traj in loader:
+
+    fog_x.Trajectory.from_dict_of_lists(
+        data_traj, path=f"/tmp/fog_x/output_{index}.vla"
+    )
+    index += 1
+    
+
+# read the data back
+for i in range(index):
+    print(fog_x.Trajectory(f"/tmp/fog_x/output_{i}.vla")["action"].keys())
\ No newline at end of file
diff --git a/examples/rtx_example/__init__.py b/examples/lerobot_loader.py
similarity index 100%
rename from examples/rtx_example/__init__.py
rename to examples/lerobot_loader.py
diff --git a/examples/openx_loader copy.py b/examples/openx_loader copy.py
new file mode 100644
index 0000000..a04d368
--- /dev/null
+++ b/examples/openx_loader copy.py	
@@ -0,0 +1,99 @@
+import argparse
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import os
+from fog_x.loader import RLDSLoader
+import fog_x
+import threading
+import time
+
+def process_data(data_traj, dataset_name, index, destination_dir, lossless):
+    try:
+        data_traj = data_traj[0]
+        steps = len(data_traj)  # Count the number of steps in the trajectory
+        return index, True, steps
+    except Exception as e:
+        print(f"Failed to process data {index}: {e}")
+        return index, False, 0
+
+def main():
+    parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.")
+    parser.add_argument("--data_dir", required=True, help="Path to the data directory")
+    parser.add_argument("--dataset_name", required=True, help="Name of the dataset")
+    parser.add_argument("--version", default="0.1.0", help="Dataset version")
+    parser.add_argument("--split", default="train", help="Data split to use")
+    parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes")
+    parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format")
+
+    args = parser.parse_args()
+
+    loader = RLDSLoader(
+        path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling = False
+    )
+
+    # train[start:end]
+    try:
+        split_starting_index = int(args.split.split("[")[1].split(":")[0])
+        print(f"Starting index: {split_starting_index}")
+    except Exception as e:
+        print(f"Failed to get starting index: {e}")
+        split_starting_index = 0
+    
+    max_concurrent_tasks = args.max_workers
+    semaphore = threading.Semaphore(max_concurrent_tasks)
+
+    total_steps = 0
+    total_trajectories = 0
+
+    with ProcessPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = []
+        retry_queue = []
+        try:
+            from tqdm import tqdm
+            for index, data_traj in tqdm(enumerate(loader), desc="Processing data", unit="trajectory"):
+                if index < split_starting_index:
+                    continue
+                semaphore.acquire()
+                future = executor.submit(process_data, data_traj, args.dataset_name, index, "", args.lossless)
+                future.add_done_callback(lambda x: semaphore.release())
+                futures.append(future)
+        except Exception as e:
+            print(f"Failed to process data: {e}")
+
+        for future in as_completed(futures):
+            try:
+                index, success, steps = future.result()
+                if success:
+                    total_steps += steps
+                    total_trajectories += 1
+                else:
+                    retry_queue.append((index, data_traj))
+            except Exception as e:
+                print(f"Error processing future: {e}")
+
+    # Retry failed tasks
+    if retry_queue:
+        print(f"Retrying {len(retry_queue)} failed tasks...")
+        with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor:
+            retry_futures = []
+            for index, data_traj in retry_queue:
+                future = retry_executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)
+                retry_futures.append(future)
+            
+            for future in as_completed(retry_futures):
+                try:
+                    index, success, steps = future.result()
+                    if not success:
+                        print(f"Failed to process data {index} after retry")
+                except Exception as e:
+                    print(f"Error processing retry future: {e}")
+
+    if total_trajectories > 0:
+        average_steps = total_steps / total_trajectories
+        print(f"Average steps per trajectory: {average_steps:.2f}")
+    else:
+        print("No trajectories were successfully processed.")
+
+    print("All tasks completed.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/openx_loader.py b/examples/openx_loader.py
new file mode 100644
index 0000000..f127d32
--- /dev/null
+++ b/examples/openx_loader.py
@@ -0,0 +1,98 @@
+import argparse
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import os
+from fog_x.loader import RLDSLoader
+import fog_x
+import threading
+import time
+
+def process_data(data_traj, dataset_name, index, destination_dir, lossless):
+    try:
+        data_traj = data_traj[0]
+        if lossless:
+            fog_x.Trajectory.from_list_of_dicts(
+                data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla",
+                lossy_compression=False
+            )
+        else:
+            fog_x.Trajectory.from_list_of_dicts(
+                data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", 
+                lossy_compression=True,
+            )
+        print(f"Processed data {index}")
+        return index, True
+    except Exception as e:
+        print(f"Failed to process data {index}: {e}")
+        return index, False
+
+def main():
+    parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.")
+    parser.add_argument("--data_dir", required=True, help="Path to the data directory")
+    parser.add_argument("--dataset_name", required=True, help="Name of the dataset")
+    parser.add_argument("--version", default="0.1.0", help="Dataset version")
+    parser.add_argument("--destination_dir", required=True, help="Destination directory for output files")
+    parser.add_argument("--split", default="train", help="Data split to use")
+    parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes")
+    parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format")
+
+    args = parser.parse_args()
+
+    loader = RLDSLoader(
+        path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling = False
+    )
+
+    # train[start:end]
+    try:
+        split_starting_index = int(args.split.split("[")[1].split(":")[0])
+        print(f"Starting index: {split_starting_index}")
+    except Exception as e:
+        print(f"Failed to get starting index: {e}")
+        split_starting_index = 0
+    
+    max_concurrent_tasks = args.max_workers
+    semaphore = threading.Semaphore(max_concurrent_tasks)
+
+    with ProcessPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = []
+        retry_queue = []
+        try:
+            from tqdm import tqdm
+            for index, data_traj in tqdm(enumerate(loader), desc="Processing data", unit="trajectory"):
+                if index < split_starting_index:
+                    continue
+                semaphore.acquire()
+                future = executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)
+                future.add_done_callback(lambda x: semaphore.release())
+                futures.append(future)
+        except Exception as e:
+            print(f"Failed to process data: {e}")
+
+        for future in as_completed(futures):
+            try:
+                index, success = future.result()
+                if not success:
+                    retry_queue.append((index, data_traj))
+            except Exception as e:
+                print(f"Error processing future: {e}")
+
+    # Retry failed tasks
+    if retry_queue:
+        print(f"Retrying {len(retry_queue)} failed tasks...")
+        with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor:
+            retry_futures = []
+            for index, data_traj in retry_queue:
+                future = retry_executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)
+                retry_futures.append(future)
+            
+            for future in as_completed(retry_futures):
+                try:
+                    index, success = future.result()
+                    if not success:
+                        print(f"Failed to process data {index} after retry")
+                except Exception as e:
+                    print(f"Error processing retry future: {e}")
+
+    print("All tasks completed.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/rlds_to_lerobot.py b/examples/rlds_to_lerobot.py
new file mode 100644
index 0000000..9ecfb3a
--- /dev/null
+++ b/examples/rlds_to_lerobot.py
@@ -0,0 +1,314 @@
+
+import shutil
+from pathlib import Path
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import torch
+import tqdm
+import yaml
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.push_dataset_to_hub.openx.transforms import OPENX_STANDARDIZATION_TRANSFORMS
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    calculate_episode_data_index,
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+with open("/home/kych/lerobot/lerobot/common/datasets/push_dataset_to_hub/openx/configs.yaml", "r") as f:
+    _openx_list = yaml.safe_load(f)
+
+OPENX_DATASET_CONFIGS = _openx_list["OPENX_DATASET_CONFIGS"]
+
+np.set_printoptions(precision=2)
+
+
+def tf_to_torch(data):
+    return torch.from_numpy(data.numpy())
+
+
+def tf_img_convert(img):
+    if img.dtype == tf.string:
+        img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)
+    elif img.dtype != tf.uint8:
+        raise ValueError(f"Unsupported image dtype: found with dtype {img.dtype}")
+    return img.numpy()
+
+
+def _broadcast_metadata_rlds(i: tf.Tensor, traj: dict) -> dict:
+    """
+    In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps"
+    entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the
+    trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
+
+    NOTE: adapted from DLimp library https://github.com/kvablack/dlimp/
+    """
+    steps = traj.pop("steps")
+
+    traj_len = tf.shape(tf.nest.flatten(steps)[0])[0]
+
+    # broadcast metadata to the length of the trajectory
+    metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj)
+
+    # put steps back in
+    assert "traj_metadata" not in steps
+    traj = {**steps, "traj_metadata": metadata}
+
+    assert "_len" not in traj
+    assert "_traj_index" not in traj
+    assert "_frame_index" not in traj
+    traj["_len"] = tf.repeat(traj_len, traj_len)
+    traj["_traj_index"] = tf.repeat(i, traj_len)
+    traj["_frame_index"] = tf.range(traj_len)
+
+    return traj
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+    openx_dataset_name: str | None = None,
+):
+    """
+    Args:
+        raw_dir (Path): _description_
+        videos_dir (Path): _description_
+        fps (int): _description_
+        video (bool): _description_
+        episodes (list[int] | None, optional): _description_. Defaults to None.
+    """
+    ds_builder = tfds.builder_from_directory(str(raw_dir))
+    dataset = ds_builder.as_dataset(
+        split="all",
+        decoders={"steps": tfds.decode.SkipDecoding()},
+    )
+
+    dataset_info = ds_builder.info
+    print("dataset_info: ", dataset_info)
+
+    ds_length = len(dataset)
+    dataset = dataset.take(ds_length)
+    # "flatten" the dataset as such we can apply trajectory level map() easily
+    # each [obs][key] has a shape of (frame_size, ...)
+    dataset = dataset.enumerate().map(_broadcast_metadata_rlds)
+
+    # we will apply the standardization transform if the dataset_name is provided
+    # if the dataset name is not provided and the goal is to convert any rlds formatted dataset
+    # search for 'image' keys in the observations
+    if openx_dataset_name is not None:
+        print(" - applying standardization transform for dataset: ", openx_dataset_name)
+        assert openx_dataset_name in OPENX_STANDARDIZATION_TRANSFORMS
+        transform_fn = OPENX_STANDARDIZATION_TRANSFORMS[openx_dataset_name]
+        dataset = dataset.map(transform_fn)
+
+        image_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["image_obs_keys"]
+    else:
+        obs_keys = dataset_info.features["steps"]["observation"].keys()
+        image_keys = [key for key in obs_keys if "image" in key]
+
+    lang_key = "language_instruction" if "language_instruction" in dataset.element_spec else None
+
+    print(" - image_keys: ", image_keys)
+    print(" - lang_key: ", lang_key)
+
+    it = iter(dataset)
+
+    ep_dicts = []
+    # Init temp path to save ep_dicts in case of crash
+    tmp_ep_dicts_dir = videos_dir.parent.joinpath("ep_dicts")
+    tmp_ep_dicts_dir.mkdir(parents=True, exist_ok=True)
+
+    # check if ep_dicts have already been saved in /tmp
+    starting_ep_idx = 0
+    saved_ep_dicts = [ep.__str__() for ep in tmp_ep_dicts_dir.iterdir()]
+    if len(saved_ep_dicts) > 0:
+        saved_ep_dicts.sort()
+        # get last ep_idx number
+        starting_ep_idx = int(saved_ep_dicts[-1][-13:-3]) + 1
+        for i in range(starting_ep_idx):
+            episode = next(it)
+            ep_dicts.append(torch.load(saved_ep_dicts[i]))
+
+    # if we user specified episodes, skip the ones not in the list
+    if episodes is not None:
+        if ds_length == 0:
+            raise ValueError("No episodes found.")
+        # convert episodes index to sorted list
+        episodes = sorted(episodes)
+
+    for ep_idx in tqdm.tqdm(range(starting_ep_idx, ds_length)):
+        episode = next(it)
+
+        # if user specified episodes, skip the ones not in the list
+        if episodes is not None:
+            if len(episodes) == 0:
+                break
+            if ep_idx == episodes[0]:
+                # process this episode
+                print(" selecting episode idx: ", ep_idx)
+                episodes.pop(0)
+            else:
+                continue  # skip
+
+        num_frames = episode["action"].shape[0]
+
+        ###########################################################
+        # Handle the episodic data
+
+        # last step of demonstration is considered done
+        done = torch.zeros(num_frames, dtype=torch.bool)
+        done[-1] = True
+        ep_dict = {}
+        langs = []  # TODO: might be located in "observation"
+
+        image_array_dict = {key: [] for key in image_keys}
+
+        # We will create the state observation tensor by stacking the state
+        # obs keys defined in the openx/configs.py
+        if openx_dataset_name is not None:
+            state_obs_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["state_obs_keys"]
+            # stack the state observations, if is None, pad with zeros
+            states = []
+            for key in state_obs_keys:
+                if key in episode["observation"]:
+                    states.append(tf_to_torch(episode["observation"][key]))
+                else:
+                    states.append(torch.zeros(num_frames, 1))  # pad with zeros
+            states = torch.cat(states, dim=1)
+            # assert states.shape == (num_frames, 8), f"states shape: {states.shape}"
+        else:
+            states = tf_to_torch(episode["observation"]["state"])
+
+        actions = tf_to_torch(episode["action"])
+        rewards = tf_to_torch(episode["reward"]).float()
+
+        # If lang_key is present, convert the entire tensor at once
+        if lang_key is not None:
+            langs = [str(x) for x in episode[lang_key]]
+
+        for im_key in image_keys:
+            imgs = episode["observation"][im_key]
+            image_array_dict[im_key] = [tf_img_convert(img) for img in imgs]
+
+        # simple assertions
+        for item in [states, actions, rewards, done]:
+            assert len(item) == num_frames
+
+        ###########################################################
+
+        # loop through all cameras
+        for im_key in image_keys:
+            img_key = f"observation.images.{im_key}"
+            imgs_array = image_array_dict[im_key]
+            imgs_array = np.array(imgs_array)
+            if video:
+                # save png images in temporary directory
+                tmp_imgs_dir = videos_dir / "tmp_images"
+                save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                # encode images to a mp4 video
+                fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                video_path = videos_dir / fname
+                encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                # clean temporary images directory
+                shutil.rmtree(tmp_imgs_dir)
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+            else:
+                ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+        if lang_key is not None:
+            ep_dict["language_instruction"] = langs
+
+        ep_dict["observation.state"] = states
+        ep_dict["action"] = actions
+        ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+        ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+        ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+        ep_dict["next.reward"] = rewards
+        ep_dict["next.done"] = done
+
+        path_ep_dict = tmp_ep_dicts_dir.joinpath(
+            "ep_dict_" + "0" * (10 - len(str(ep_idx))) + str(ep_idx) + ".pt"
+        )
+        torch.save(ep_dict, path_ep_dict)
+
+        ep_dicts.append(ep_dict)
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    keys = [key for key in data_dict if "observation.images." in key]
+    for key in keys:
+        if video:
+            features[key] = VideoFrame()
+        else:
+            features[key] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if "observation.velocity" in data_dict:
+        features["observation.velocity"] = Sequence(
+            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "observation.effort" in data_dict:
+        features["observation.effort"] = Sequence(
+            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "language_instruction" in data_dict:
+        features["language_instruction"] = Value(dtype="string", id=None)
+
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.reward"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    # hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+dataset_name = "nyu_door_opening_surprising_effectiveness"
+# load the rlds dataset
+dataset = load_from_raw(
+    raw_dir=f"/mnt/data/fog_x/rlds/{dataset_name}/",
+    videos_dir=Path(f"/mnt/data/fog_x/hf/{dataset_name}/videos"),
+    fps=12,
+    video=True,
+    openx_dataset_name=dataset_name,
+)
+
+# convert to hf dataset
+hf_dataset = to_hf_dataset(dataset, video=True)
+
+# save to hf
+hf_dataset.save_to_disk("/mnt/data/fog_x/hf/nyu_door_opening_surprising_effectiveness")
\ No newline at end of file
diff --git a/examples/rtx_example/load.py b/examples/rtx_example/load.py
deleted file mode 100644
index 2e90540..0000000
--- a/examples/rtx_example/load.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import fog_x
-
-dataset = fog_x.dataset.Dataset(
-    name="demo_ds",
-    path="~/test_dataset",
-)
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[:1]",
-)
-
-dataset.export(format="rtx")
diff --git a/examples/rtx_example/merge.py b/examples/rtx_example/merge.py
deleted file mode 100644
index 2029ae7..0000000
--- a/examples/rtx_example/merge.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import fog_x
-
-dataset = fog_x.dataset.Dataset(
-    name="demo_ds",
-    path="~/test_dataset",
-)
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[:2]",
-    additional_metadata={"collector": "User 1", "custom_tag": "Partition_1"},
-)
-
-dataset.load_rtx_episodes(
-    name="berkeley_autolab_ur5",
-    split="train[3:5]",
-    additional_metadata={"collector": "User 2", "custom_tag": "Partition_2"},
-)
-# dataset.num_episodes == 4
-
-# query the dataset
-episode_info = dataset.get_episode_info()
-print(episode_info)
-# only get the episodes with custom_tag == "Partition_1"
-metadata = episode_info.filter(episode_info["custom_tag"] == "Partition_1")
-episodes = dataset.read_by(metadata)
-
-# read the episodes
-for episode in episodes:
-    print(episode)
diff --git a/examples/summarize_dataset.py b/examples/summarize_dataset.py
new file mode 100644
index 0000000..0344d5f
--- /dev/null
+++ b/examples/summarize_dataset.py
@@ -0,0 +1,19 @@
+import fog_x 
+from fog_x.loader import RLDSLoader
+
+path = "/home/kych/datasets/rtx"
+dataset_name = "fractal20220817_data"
+version = "0.1.0"
+split = "train"
+
+loader = RLDSLoader(path=f"{path}/{dataset_name}/{version}", split=split, shuffling=False)
+
+data = loader[0][0]
+for k, v in data.items():
+    print(k)
+    if k == "observation" or k == "action":
+        for k2, v2 in v.items():
+            print(k, k2, v2.shape, v2.dtype)
+    else:
+        print(k, v.shape, v.dtype)
+
diff --git a/examples/vla_file_debugger.py b/examples/vla_file_debugger.py
new file mode 100644
index 0000000..33e0e8f
--- /dev/null
+++ b/examples/vla_file_debugger.py
@@ -0,0 +1,122 @@
+import os
+import numpy as np
+from fog_x.trajectory import Trajectory
+from fog_x.utils import _flatten
+import imageio
+from fog_x.loader import RLDSLoader
+
+def load_ffv1_trajectory(path):
+    traj = Trajectory(path,)
+    return _flatten(traj.load())
+
+def load_vla_trajectory(path):
+    traj = Trajectory(path)
+    return _flatten(traj.load())
+
+def load_rlds_trajectory(path, dataset_name, version, split, index):
+    loader = RLDSLoader(path=f"{path}/{dataset_name}/{version}", split=split, shuffling=False)
+    data_traj = loader[index]
+    
+    data = {}
+    # convert from a list of dicts to a dict of lists
+    traj_len = len(data_traj)
+    for i in range(traj_len):
+        data_traj[i] = _flatten(data_traj[i])
+        for k, v in data_traj[i].items():
+            if k == "observation/natural_language_instruction":
+                print(v)
+                continue
+            if k not in data:
+                data[k] = np.empty((traj_len, *v.shape))
+            data[k][i] = v
+    return data
+
+def save_traj_images_to_dir(traj_data, dir_path):
+    os.makedirs(dir_path, exist_ok=True)
+    for i in range(len(traj_data["observation/image"])):
+        imageio.imwrite(f"{dir_path}/{i}.png", traj_data["observation/image"][i].astype(np.uint8))
+
+def compare_trajectories(ffv1_data, vla_data, rlds_data, file_name):
+    print(f"\nComparing FFV1, VLA, and RLDS trajectories for {file_name}:")
+    
+    # Compare keys
+    ffv1_keys = set(ffv1_data.keys())
+    vla_keys = set(vla_data.keys())
+    rlds_keys = set(rlds_data.keys())
+    
+    print(f"FFV1 keys: {ffv1_keys}")
+    print(f"VLA keys: {vla_keys}")
+    print(f"RLDS keys: {rlds_keys}")
+    
+    common_keys = ffv1_keys.intersection(vla_keys).intersection(rlds_keys)
+    
+    # Compare data for common keys
+    for key in common_keys:
+        if key == "observation/natural_language_instruction":
+            continue
+        ffv1_array = ffv1_data[key]
+        vla_array = vla_data[key]
+        rlds_array = rlds_data[key]
+        
+        print(f"\nComparing '{key}':")
+        print(f"  FFV1 shape: {ffv1_array.shape}, dtype: {ffv1_array.dtype}")
+        print(f"  VLA shape: {vla_array.shape}, dtype: {vla_array.dtype}")
+        print(f"  RLDS shape: {rlds_array.shape}, dtype: {rlds_array.dtype}")
+        
+        if ffv1_array.shape == vla_array.shape == rlds_array.shape: #and ffv1_array.dtype == vla_array.dtype == rlds_array.dtype:
+            if np.allclose(ffv1_array, vla_array) and np.allclose(ffv1_array, rlds_array):
+                continue
+            else:
+                diff_ffv1_vla = np.abs(ffv1_array - vla_array)
+                diff_ffv1_rlds = np.abs(ffv1_array - rlds_array)
+                diff_vla_rlds = np.abs(vla_array - rlds_array)
+                print(f"  Max difference FFV1-VLA: {np.max(diff_ffv1_vla)}")
+                print(f"  Max difference FFV1-RLDS: {np.max(diff_ffv1_rlds)}")
+                print(f"  Max difference VLA-RLDS: {np.max(diff_vla_rlds)}")
+                print(f"  Mean difference FFV1-VLA: {np.mean(diff_ffv1_vla)}")
+                print(f"  Mean difference FFV1-RLDS: {np.mean(diff_ffv1_rlds)}")
+                print(f"  Mean difference VLA-RLDS: {np.mean(diff_vla_rlds)}")
+                if key == "observation/image":
+                    print("ffv1_array[0]: ", ffv1_array[0])
+                    print("vla_array[0]: ", vla_array[0])
+                    print("rlds_array[0]: ", rlds_array[0])
+                    save_traj_images_to_dir(ffv1_data, f"{file_name}_ffv1")
+                    save_traj_images_to_dir(vla_data, f"{file_name}_vla")
+                    save_traj_images_to_dir(rlds_data, f"{file_name}_rlds")
+        else:
+            print("  Shape or dtype mismatch")
+            print(f" ffv1: {np.sum(ffv1_array - np.array(rlds_array))}")
+            print(f" vla: {np.sum(vla_array - np.array(rlds_array))}")
+
+def main():
+    # dataset_name = "bridge"
+    dataset_name = "fractal20220817_data"
+    base_path = f"/home/kych/datasets/{dataset_name}"
+    # base_path = "/mnt/data/fog_x"
+    ffv1_dir = os.path.join(base_path, "ffv1", dataset_name)
+    vla_dir = os.path.join(base_path, "vla", dataset_name)
+    rlds_dir = "/home/kych/datasets/rtx"
+    version = "0.1.0"
+    split = "train"
+
+    # Get all .vla files in the ffv1 directory
+    vla_files = ["output_{}.vla".format(i) for i in range(1)]
+
+    for file_name in vla_files:
+        ffv1_file = os.path.join(ffv1_dir, file_name)
+        vla_file = os.path.join(vla_dir, file_name)
+        index = int(file_name.split("_")[1].split(".")[0])
+
+        if not os.path.exists(vla_file):
+            print(f"Skipping {file_name}: VLA file not found")
+            continue
+
+        print(f"\nProcessing {file_name}")
+        ffv1_data = load_ffv1_trajectory(ffv1_file)
+        vla_data = load_vla_trajectory(vla_file)
+        rlds_data = load_rlds_trajectory(rlds_dir, dataset_name, version, split, index)
+        
+        compare_trajectories(ffv1_data, vla_data, rlds_data, file_name)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/vla_loader.py b/examples/vla_loader.py
new file mode 100644
index 0000000..b7e53bb
--- /dev/null
+++ b/examples/vla_loader.py
@@ -0,0 +1,10 @@
+from fog_x.loader import VLALoader
+import fog_x
+import os
+
+
+loader = VLALoader("/tmp/fog_x/vla/berkeley_autolab_ur5/*.vla")
+for index, data_traj in enumerate(loader):
+
+    print(data_traj.load())
+    index += 1
\ No newline at end of file
diff --git a/examples/vla_to_h5.py b/examples/vla_to_h5.py
new file mode 100644
index 0000000..df8dd06
--- /dev/null
+++ b/examples/vla_to_h5.py
@@ -0,0 +1,103 @@
+import fog_x 
+import os
+import argparse
+from concurrent.futures import ProcessPoolExecutor, as_completed, TimeoutError
+from tqdm import tqdm
+import threading
+from fog_x.loader import NonShuffleVLALoader
+import h5py
+import time
+
+def process_data(trajectory, dataset_name, index, destination_dir):
+    try:    
+        print(f"Processing data {index}")
+        if trajectory is None:
+            print(f"Trajectory is None for index {index}")
+            return index, False
+        write_to_h5(trajectory, dataset_name, index, destination_dir)
+        return index, True
+    except Exception as e:
+        print(f"Failed to process data {index}: {e}")
+        return index, False
+
+def write_to_h5(trajectory, dataset_name, index, destination_dir):
+    print(trajectory.keys())
+    try:
+        with h5py.File(f"{destination_dir}/{dataset_name}/output_{index}.h5", "w") as f:
+            for k in trajectory.keys():
+                v = trajectory[k]
+                print(k, v.shape)
+                
+                f.create_dataset(k, data=v, compression="gzip", compression_opts=9)
+    except Exception as e:
+        print(f"Failed to write to h5 {index}: {e}")
+
+    # except Exception as e:
+    #     print(f"Failed to process data {index}: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert VLA data to HDF5 format.")
+    parser.add_argument("--data_dir", required=True, help="Path to the VLA data directory")
+    parser.add_argument("--dataset_name", required=True, help="Name of the dataset")
+    parser.add_argument("--destination_dir", required=True, help="Destination directory for output HDF5 files")
+    parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes")
+    parser.add_argument("--timeout", type=int, default=20, help="Timeout for each task in seconds")
+
+    args = parser.parse_args()
+
+    vla_path = os.path.join(args.data_dir, args.dataset_name, "*.vla")
+    cache_dir = os.path.join("/mnt/data/fog_x/cache/", args.dataset_name)
+    print(vla_path, cache_dir)
+    loader = NonShuffleVLALoader(vla_path, cache_dir=cache_dir)
+
+    os.makedirs(os.path.join(args.destination_dir, args.dataset_name), exist_ok=True)
+
+    max_concurrent_tasks = args.max_workers
+    semaphore = threading.Semaphore(max_concurrent_tasks)
+
+    with ProcessPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = []
+        retry_queue = []
+        try:
+            for index, trajectory in tqdm(enumerate(loader), desc="Submitting tasks", unit="trajectory"):
+                semaphore.acquire()
+                future = executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir)
+                future.add_done_callback(lambda x: semaphore.release())
+                futures.append(future)
+        except Exception as e:
+            print(f"Failed to submit tasks: {e}")
+
+        for future in tqdm(as_completed(futures), total=len(futures), desc="Processing tasks"):
+            try:
+                index, success = future.result(timeout=args.timeout)
+                if not success:
+                    retry_queue.append((index, trajectory))
+            except TimeoutError:
+                print(f"Task for index {index} timed out")
+                retry_queue.append((index, trajectory))
+            except Exception as e:
+                print(f"Error processing future: {e}")
+
+    # Retry failed tasks
+    if retry_queue:
+        print(f"Retrying {len(retry_queue)} failed tasks...")
+        with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor:
+            retry_futures = []
+            for index, trajectory in retry_queue:
+                future = retry_executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir)
+                retry_futures.append(future)
+            
+            for future in tqdm(as_completed(retry_futures), total=len(retry_futures), desc="Processing retry tasks"):
+                try:
+                    index, success = future.result(timeout=args.timeout)
+                    if not success:
+                        print(f"Failed to process data {index} after retry")
+                except TimeoutError:
+                    print(f"Retry task for index {index} timed out")
+                except Exception as e:
+                    print(f"Error processing retry future: {e}")
+
+    print("All tasks completed.")
+
+if __name__ == "__main__":
+    main()
diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py
new file mode 100644
index 0000000..4204062
--- /dev/null
+++ b/fog_x/DLdataset.py
@@ -0,0 +1,423 @@
+import inspect
+import string
+from functools import partial
+from typing import Any, Callable, Dict, Sequence, Union
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from tensorflow_datasets.core.dataset_builder import DatasetBuilder
+
+from dlimp.utils import parallel_vmap, vmap
+from .dataset import VLADataset
+import h5py
+def _wrap(f, is_flattened):
+    """Wraps a method to return a DLataset instead of a tf.data.Dataset."""
+
+    def wrapper(*args, **kwargs):
+        result = f(*args, **kwargs)
+        if not isinstance(result, DLataset) and isinstance(result, tf.data.Dataset):
+            # make the result a subclass of DLataset and the original class
+            result.__class__ = type(
+                "DLataset", (DLataset, type(result)), DLataset.__dict__.copy()
+            )
+            # propagate the is_flattened flag
+            if is_flattened is None:
+                result.is_flattened = f.__self__.is_flattened
+            else:
+                result.is_flattened = is_flattened
+        return result
+
+    return wrapper
+
+
+class DLataset(tf.data.Dataset):
+    """A DLimp Dataset. This is a thin wrapper around tf.data.Dataset that adds some utilities for working
+    with datasets of trajectories.
+
+    A DLataset starts out as dataset of trajectories, where each dataset element is a single trajectory. A
+    dataset element is always a (possibly nested) dictionary from strings to tensors; however, a trajectory
+    has the additional property that each tensor has the same leading dimension, which is the trajectory
+    length. Each element of the trajectory is known as a frame.
+
+    A DLataset is just a tf.data.Dataset, so you can always use standard methods like `.map` and `.filter`.
+    However, a DLataset is also aware of the difference between trajectories and frames, so it provides some
+    additional methods. To perform a transformation at the trajectory level (e.g., restructuring, relabeling,
+    truncating), use `.traj_map`. To perform a transformation at the frame level (e.g., image decoding,
+    resizing, augmentations) use `.frame_map`.
+
+    Once there are no more trajectory-level transformation to perform, you can convert to DLataset to a
+    dataset of frames using `.flatten`. You can still use `.frame_map` after flattening, but using `.traj_map`
+    will raise an error.
+    """
+
+    def __getattribute__(self, name):
+        # monkey-patches tf.data.Dataset methods to return DLatasets
+        attr = super().__getattribute__(name)
+        if inspect.ismethod(attr):
+            return _wrap(attr, None)
+        return attr
+
+    def _apply_options(self):
+        """Applies some default options for performance."""
+        options = tf.data.Options()
+        options.autotune.enabled = True
+        options.deterministic = False
+        options.experimental_optimization.apply_default_optimizations = True
+        options.experimental_optimization.map_fusion = True
+        options.experimental_optimization.map_and_filter_fusion = True
+        options.experimental_optimization.inject_prefetch = False
+        options.experimental_warm_start = True
+        return self.with_options(options)
+
+    def with_ram_budget(self, gb: int) -> "DLataset":
+        """Sets the RAM budget for the dataset. The default is half of the available memory.
+
+        Args:
+            gb (int): The RAM budget in GB.
+        """
+        options = tf.data.Options()
+        options.autotune.ram_budget = gb * 1024 * 1024 * 1024  # GB --> Bytes
+        return self.with_options(options)
+
+    @staticmethod
+    def from_tfrecords(
+        dir_or_paths: Union[str, Sequence[str]],
+        shuffle: bool = True,
+        num_parallel_reads: int = tf.data.AUTOTUNE,
+    ) -> "DLataset":
+        """Creates a DLataset from tfrecord files. The type spec of the dataset is inferred from the first file. The
+        only constraint is that each example must be a trajectory where each entry is either a scalar, a tensor of shape
+        (1, ...), or a tensor of shape (T, ...), where T is the length of the trajectory.
+
+        Args:
+            dir_or_paths (Union[str, Sequence[str]]): Either a directory containing .tfrecord files, or a list of paths
+                to tfrecord files.
+            shuffle (bool, optional): Whether to shuffle the tfrecord files. Defaults to True.
+            num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This
+                can use an excessive amount of memory if reading from cloud storage; decrease if necessary.
+        """
+        if isinstance(dir_or_paths, str):
+            paths = tf.io.gfile.glob(tf.io.gfile.join(dir_or_paths, "*.tfrecord"))
+        else:
+            paths = dir_or_paths
+
+        if len(paths) == 0:
+            raise ValueError(f"No tfrecord files found in {dir_or_paths}")
+
+        if shuffle:
+            paths = tf.random.shuffle(paths)
+
+        # extract the type spec from the first file
+        type_spec = _get_type_spec(paths[0])
+
+        # read the tfrecords (yields raw serialized examples)
+        dataset = _wrap(tf.data.TFRecordDataset, False)(
+            paths,
+            num_parallel_reads=num_parallel_reads,
+        )._apply_options()
+
+        # decode the examples (yields trajectories)
+        dataset = dataset.traj_map(partial(_decode_example, type_spec=type_spec))
+
+        # broadcast traj metadata, as well as add some extra metadata (_len, _traj_index, _frame_index)
+        dataset = dataset.enumerate().traj_map(_broadcast_metadata)
+
+        return dataset
+
+    @staticmethod
+    def from_rlds(
+        builder: DatasetBuilder,
+        split: str = "train",
+        shuffle: bool = True,
+        num_parallel_reads: int = tf.data.AUTOTUNE,
+    ) -> "DLataset":
+        """Creates a DLataset from the RLDS format (which is a special case of the TFDS format).
+
+        Args:
+            builder (DatasetBuilder): The TFDS dataset builder to load the dataset from.
+            data_dir (str): The directory to load the dataset from.
+            split (str, optional): The split to load, specified in TFDS format. Defaults to "train".
+            shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True.
+            num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This
+                can use an excessive amount of memory if reading from cloud storage; decrease if necessary.
+        """
+        dataset = _wrap(builder.as_dataset, False)(
+            split=split,
+            shuffle_files=shuffle,
+            decoders={"steps": tfds.decode.SkipDecoding()},
+            read_config=tfds.ReadConfig(
+                skip_prefetch=True,
+                num_parallel_calls_for_interleave_files=num_parallel_reads,
+                interleave_cycle_length=num_parallel_reads,
+            ),
+        )._apply_options()
+
+        dataset = dataset.enumerate().traj_map(_broadcast_metadata_rlds)
+
+        return dataset
+        
+    @staticmethod
+    def from_vla(
+        dataset_dir: str,
+        dataset_name : str,
+        split: str = "train",
+        shuffle: bool = True,
+        num_parallel_reads: int = tf.data.AUTOTUNE,
+    ) -> "DLataset":
+        """Creates a DLataset from the RLDS format (which is a special case of the TFDS format).
+
+        Args:
+            builder (DatasetBuilder): The TFDS dataset builder to load the dataset from.
+            data_dir (str): The directory to load the dataset from.
+            split (str, optional): The split to load, specified in TFDS format. Defaults to "train".
+            shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True.
+            num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This
+                can use an excessive amount of memory if reading from cloud storage; decrease if necessary.
+        """
+        path = f"{dataset_dir}/{dataset_name}"
+        vla_dataset = VLADataset(path, split, shuffle=shuffle)
+        
+        step_spec = vla_dataset.get_tf_schema()
+        # Generator function
+        def generator():
+            for ts in vla_dataset:
+                output = {"steps" : ts}
+                
+                yield output
+                
+
+        # Create dataset
+        output_signature = {"steps" : tf.nest.map_structure(
+            lambda spec: tf.TensorSpec(shape=spec.shape, dtype=spec.dtype), step_spec
+        )}
+        print(output_signature)
+        
+        dataset = _wrap(tf.data.Dataset.from_generator, False)(
+            generator,
+            output_signature=output_signature
+        )
+
+
+        dataset = dataset.enumerate().traj_map(_broadcast_metadata_rlds)
+
+        return dataset
+
+    
+    def map(
+        self,
+        fn: Callable[[Dict[str, Any]], Dict[str, Any]],
+        num_parallel_calls=tf.data.AUTOTUNE,
+        **kwargs,
+    ) -> "DLataset":
+        return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs)
+
+    def traj_map(
+        self,
+        fn: Callable[[Dict[str, Any]], Dict[str, Any]],
+        num_parallel_calls=tf.data.AUTOTUNE,
+        **kwargs,
+    ) -> "DLataset":
+        """Maps a function over the trajectories of the dataset. The function should take a single trajectory
+        as input and return a single trajectory as output.
+        """
+        if self.is_flattened:
+            raise ValueError("Cannot call traj_map on a flattened dataset.")
+        return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs)
+
+    def frame_map(
+        self,
+        fn: Callable[[Dict[str, Any]], Dict[str, Any]],
+        num_parallel_calls=tf.data.AUTOTUNE,
+        **kwargs,
+    ) -> "DLataset":
+        """Maps a function over the frames of the dataset. The function should take a single frame as input
+        and return a single frame as output.
+        """
+        if self.is_flattened:
+            return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs)
+        else:
+            return super().map(
+                parallel_vmap(fn, num_parallel_calls=num_parallel_calls),
+                num_parallel_calls=num_parallel_calls,
+                **kwargs,
+            )
+
+    def flatten(self, *, num_parallel_calls=tf.data.AUTOTUNE) -> "DLataset":
+        """Flattens the dataset of trajectories into a dataset of frames."""
+        if self.is_flattened:
+            raise ValueError("Dataset is already flattened.")
+        dataset = self.interleave(
+            lambda traj: tf.data.Dataset.from_tensor_slices(traj),
+            cycle_length=num_parallel_calls,
+            num_parallel_calls=num_parallel_calls,
+        )
+        dataset.is_flattened = True
+        return dataset
+
+    def iterator(self, *, prefetch=tf.data.AUTOTUNE):
+        if prefetch == 0:
+            return self.as_numpy_iterator()
+        return self.prefetch(prefetch).as_numpy_iterator()
+
+    @staticmethod
+    def choose_from_datasets(datasets, choice_dataset, stop_on_empty_dataset=True):
+        if not isinstance(datasets[0], DLataset):
+            raise ValueError("Please pass DLatasets to choose_from_datasets.")
+        return _wrap(tf.data.Dataset.choose_from_datasets, datasets[0].is_flattened)(
+            datasets, choice_dataset, stop_on_empty_dataset=stop_on_empty_dataset
+        )
+
+    @staticmethod
+    def sample_from_datasets(
+        datasets,
+        weights=None,
+        seed=None,
+        stop_on_empty_dataset=False,
+        rerandomize_each_iteration=None,
+    ):
+        if not isinstance(datasets[0], DLataset):
+            raise ValueError("Please pass DLatasets to sample_from_datasets.")
+        return _wrap(tf.data.Dataset.sample_from_datasets, datasets[0].is_flattened)(
+            datasets,
+            weights=weights,
+            seed=seed,
+            stop_on_empty_dataset=stop_on_empty_dataset,
+            rerandomize_each_iteration=rerandomize_each_iteration,
+        )
+
+    @staticmethod
+    def zip(*args, datasets=None, name=None):
+        if datasets is not None:
+            raise ValueError("Please do not pass `datasets=` to zip.")
+        if not isinstance(args[0], DLataset):
+            raise ValueError("Please pass DLatasets to zip.")
+        return _wrap(tf.data.Dataset.zip, args[0].is_flattened)(*args, name=name)
+
+
+def _decode_example(
+    example_proto: tf.Tensor, type_spec: Dict[str, tf.TensorSpec]
+) -> Dict[str, tf.Tensor]:
+    features = {key: tf.io.FixedLenFeature([], tf.string) for key in type_spec.keys()}
+    parsed_features = tf.io.parse_single_example(example_proto, features)
+    parsed_tensors = {
+        key: tf.io.parse_tensor(parsed_features[key], spec.dtype)
+        if spec is not None
+        else parsed_features[key]
+        for key, spec in type_spec.items()
+    }
+
+    for key in parsed_tensors:
+        if type_spec[key] is not None:
+            parsed_tensors[key] = tf.ensure_shape(
+                parsed_tensors[key], type_spec[key].shape
+            )
+
+    return parsed_tensors
+
+
+def _get_type_spec(path: str) -> Dict[str, tf.TensorSpec]:
+    """Get a type spec from a tfrecord file.
+
+    Args:
+        path (str): Path to a single tfrecord file.
+
+    Returns:
+        dict: A dictionary mapping feature names to tf.TensorSpecs.
+    """
+    data = next(iter(tf.data.TFRecordDataset(path))).numpy()
+    example = tf.train.Example()
+    example.ParseFromString(data)
+
+    printable_chars = set(bytes(string.printable, "utf-8"))
+
+    out = {}
+    for key, value in example.features.feature.items():
+        data = value.bytes_list.value[0]
+        # stupid hack to deal with strings that are not encoded as tensors
+        if all(char in printable_chars for char in data):
+            out[key] = None
+            continue
+        tensor_proto = tf.make_tensor_proto([])
+        tensor_proto.ParseFromString(data)
+        dtype = tf.dtypes.as_dtype(tensor_proto.dtype)
+        shape = [d.size for d in tensor_proto.tensor_shape.dim]
+        if shape:
+            shape[0] = None  # first dimension is trajectory length, which is variable
+        out[key] = tf.TensorSpec(shape=shape, dtype=dtype)
+
+    return out
+
+
+def _broadcast_metadata(
+    i: tf.Tensor, traj: Dict[str, tf.Tensor]
+) -> Dict[str, tf.Tensor]:
+    """
+    Each element of a dlimp dataset is a trajectory. This means each entry must either have a leading dimension equal to
+    the length of the trajectory, have a leading dimension of 1, or be a scalar. Entries with a leading dimension of 1
+    and scalars are assumed to be trajectory-level metadata. This function broadcasts these entries to the length of the
+    trajectory, as well as adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
+    """
+    # get the length of each dict entry
+    traj_lens = {
+        k: tf.shape(v)[0] if len(v.shape) > 0 else None for k, v in traj.items()
+    }
+
+    # take the maximum length as the canonical length (elements should either be the same length or length 1)
+    traj_len = tf.reduce_max([l for l in traj_lens.values() if l is not None])
+
+    for k in traj:
+        # broadcast scalars to the length of the trajectory
+        if traj_lens[k] is None:
+            traj[k] = tf.repeat(traj[k], traj_len)
+            traj_lens[k] = traj_len
+
+        # broadcast length-1 elements to the length of the trajectory
+        if traj_lens[k] == 1:
+            traj[k] = tf.repeat(traj[k], traj_len, axis=0)
+            traj_lens[k] = traj_len
+
+    asserts = [
+        # make sure all the lengths are the same
+        tf.assert_equal(
+            tf.size(tf.unique(tf.stack(list(traj_lens.values()))).y),
+            1,
+            message="All elements must have the same length.",
+        ),
+    ]
+
+    assert "_len" not in traj
+    assert "_traj_index" not in traj
+    assert "_frame_index" not in traj
+    traj["_len"] = tf.repeat(traj_len, traj_len)
+    traj["_traj_index"] = tf.repeat(i, traj_len)
+    traj["_frame_index"] = tf.range(traj_len)
+
+    with tf.control_dependencies(asserts):
+        return traj
+
+
+def _broadcast_metadata_rlds(i: tf.Tensor, traj: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps"
+    entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the
+    trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
+    """
+    steps = traj.pop("steps")
+
+    traj_len = tf.shape(tf.nest.flatten(steps)[0])[0]
+
+    # broadcast metadata to the length of the trajectory
+    metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj)
+
+    # put steps back in
+    assert "traj_metadata" not in steps
+    traj = {**steps, "traj_metadata": metadata}
+
+    assert "_len" not in traj
+    assert "_traj_index" not in traj
+    assert "_frame_index" not in traj
+    traj["_len"] = tf.repeat(traj_len, traj_len)
+    traj["_traj_index"] = tf.repeat(i, traj_len)
+    traj["_frame_index"] = tf.range(traj_len)
+
+    return traj
\ No newline at end of file
diff --git a/fog_x/__init__.py b/fog_x/__init__.py
index fc2c642..ce2a2f1 100644
--- a/fog_x/__init__.py
+++ b/fog_x/__init__.py
@@ -3,10 +3,14 @@
 __root_dir__ = os.path.dirname(os.path.abspath(__file__))
 
 
-from fog_x import dataset, episode, feature
-from fog_x.dataset import Dataset
+# from fog_x import dataset, episode, feature
+# from fog_x.dataset import Dataset
+# from fog_x import trajectory
 
-all = ["dataset", "feature", "episode", "Dataset"]
+from fog_x.feature import FeatureType
+from fog_x.trajectory import Trajectory
+
+all = ["trajectory"]
 
 import logging
 
diff --git a/fog_x/dataset.py b/fog_x/dataset.py
index f20d343..65ee6fe 100644
--- a/fog_x/dataset.py
+++ b/fog_x/dataset.py
@@ -1,744 +1,62 @@
-import io
-import logging
 import os
-from typing import Any, Dict, List, Optional, Tuple
-import subprocess
+from typing import Any, Dict, List, Optional, Text
+from fog_x.loader.vla import VLALoader, NonShuffleVLALoader
+from fog_x.utils import data_to_tf_schema
 import numpy as np
-import polars
-import pandas
 
-from fog_x.database import (
-    DatabaseConnector,
-    DatabaseManager,
-    DataFrameConnector,
-    LazyFrameConnector,
-    PolarsConnector,
-)
-from fog_x.episode import Episode
-from fog_x.feature import FeatureType
-
-logger = logging.getLogger(__name__)
-
-
-
-def convert_to_h264(input_file, output_file):
-    
-    # FFmpeg command to convert video to H.264
-    command = [
-        'ffmpeg',
-        '-i', input_file,    # Input file
-        '-loglevel', 'error', # Suppress the logs
-        '-vcodec', 'h264', # Specify the codec
-        output_file          # Output file
-    ]
-    subprocess.run(command)
-
-def create_cloud_bucket_if_not_exist(provider, bucket_name, dir_name):
-    logger.info(f"Creating bucket '{bucket_name}' in cloud provider '{provider}' with folder '{dir_name}'...")
-    if provider == "s3":
-        import boto3
-        s3_client = boto3.client('s3')
-        # s3_client.create_bucket(Bucket=bucket_name)
-        s3_client.put_object(Bucket=bucket_name, Key=f"{dir_name}/")
-        logger.info(f"Bucket '{bucket_name}' created in AWS S3.")
-    elif provider == "gs":
-        from google.cloud import storage
-        """Create a folder in a Google Cloud Storage bucket if it does not exist."""
-        storage_client = storage.Client()
-        bucket = storage_client.bucket(bucket_name)
-
-        # Ensure the folder name ends with a '/'
-        if not dir_name.endswith('/'):
-            dir_name += '/'
-
-        # Check if folder exists by trying to list objects with the folder prefix
-        blobs = storage_client.list_blobs(bucket_name, prefix=dir_name, delimiter='/')
-        exists = any(blob.name == dir_name for blob in blobs)
-
-        if not exists:
-            # Create an empty blob to simulate a folder
-            blob = bucket.blob(dir_name)
-            blob.upload_from_string('')
-            print(f"Folder '{dir_name}' created.")
-        else:
-            print(f"Folder '{dir_name}' already exists.")
-    else:
-        raise ValueError(f"Unsupported cloud provider '{provider}'.")
-
-class Dataset:
+class VLADataset:
     """
-    Create or load from a new dataset.
+    1. figure out the path to the dataset
+    2. shuffling / training management 
     """
-
-    def __init__(
-        self,
-        name: str,
-        path: str = None,
-        replace_existing: bool = False,
-        features: Dict[
-            str, FeatureType
-        ] = {},  # features to be stored {name: FeatureType}
-        enable_feature_inference=True,  # whether additional features can be inferred
-        episode_info_connector: DatabaseConnector = None,
-        step_data_connector: DatabaseConnector = None,
-        storage: Optional[str] = None,
-    ) -> None:
+    def __init__(self, 
+                 path: Text,
+                 split: Text, 
+                 shuffle: bool = True,
+                 format: Optional[Text] = None):
         """
-
+        init method for Dataset class
         Args:
-            name (str): Name of this dataset. Used as the directory name when exporting.
-            path (str): Required. Local path of where this dataset should be stored.
-            features (optional Dict[str, FeatureType]): Description of `param1`.
-            enable_feature_inference (bool): enable inferring additional FeatureTypes
-
-        Example:
-        ```
-        >>> dataset = fog_x.Dataset('my_dataset', path='~/fog_x/my_dataset`)
-        ```
-
-        TODO:
-            * is replace_existing actually used anywhere?
-        """
-        self.name = name
-
-        if path.startswith("."): # relative path
-            path = os.path.abspath(path).removesuffix("/")
-        elif path.startswith("~"): # home directory
-            path = os.path.expanduser(path).removesuffix("/")
-        elif path.startswith("/"): # absolute path
-            path = path.removesuffix("/")
-        elif path.startswith("s3://") or path.startswith("gs://"):
-            path = path.removesuffix("/")
-        else:
-            raise ValueError("Unsupported path format. Please use absolute path or relative path starting with '.' or '~'.")
-
-        logger.info(f"Dataset path: {path}")
+            paths Text: path-like to the dataset
+                it can be a glob pattern or a directory
+                if it starts with gs:// it will be treated as a google cloud storage path with rlds format 
+                if it ends with .h5 it will be treated as a hdf5 file
+                if it ends with .tfrecord it will be treated as a rlds file
+                if it ends with .vla it will be treated as a vla file
+            split (Text): split of the dataset
+            format (Optional[Text]): format of the dataset. Auto-detected if None. Defaults to None.
+                we assume that the format is the same for all files in the dataset
+        """    
         self.path = path
-        if path is None:
-            raise ValueError("Path is required")
-        # create the folder if path doesn't exist
-        if self.path.startswith("/") and not os.path.exists(path):
-            logger.info(f"Creating directory {path}")
-            os.makedirs(path)
-
-        self.replace_existing = replace_existing
-        self.features = features
-        self.enable_feature_inference = enable_feature_inference
-        if episode_info_connector is None:
-                episode_info_connector = DataFrameConnector(f"{path}")
-        
-        if step_data_connector is None:
-            if self.path.startswith("/") and not os.path.exists(f"{path}/{name}"):
-                os.makedirs(f"{path}/{name}")
-            try: 
-                step_data_connector = LazyFrameConnector(f"{path}/{name}")
-            except:
-                logger.info(f"Path does not exist. ({path}/{name})")
-                cloud_provider = path[:2]
-                bucket_name = path[5:]
-                create_cloud_bucket_if_not_exist(cloud_provider, bucket_name, f"{name}/")
-                step_data_connector = LazyFrameConnector(f"{path}/{name}") 
-        self.db_manager = DatabaseManager(episode_info_connector, step_data_connector)
-        self.db_manager.initialize_dataset(self.name, features)
-
-        self.storage = storage
-        self.obs_keys = []
-        self.act_keys = []
-        self.step_keys = []
-
-    def new_episode(self, metadata: Optional[Dict[str, Any]] = None) -> Episode:
-        """
-        Create a new episode / trajectory.
-
-        Returns:
-            Episode
-
-        TODO:
-            * support multiple processes writing to the same episode
-            * close the previous episode if not closed
-        """
-        return Episode(
-            metadata=metadata,
-            features=self.features,
-            enable_feature_inference=self.enable_feature_inference,
-            db_manager=self.db_manager,
-        )
-
-    def _get_tf_feature_dicts(
-        self, obs_keys: List[str], act_keys: List[str], step_keys: List[str]
-    ) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
-        """
-        Get the tensorflow feature dictionaries.
-        """
-        observation_tf_dict = {}
-        action_tf_dict = {}
-        step_tf_dict = {}
-
-        for k in obs_keys:
-            observation_tf_dict[k] = self.features[k].to_tf_feature_type()
-
-        for k in act_keys:
-            action_tf_dict[k] = self.features[k].to_tf_feature_type()
-
-        for k in step_keys:
-            step_tf_dict[k] = self.features[k].to_tf_feature_type()
-
-        return observation_tf_dict, action_tf_dict, step_tf_dict
-
-    def export(
-        self,
-        export_path: Optional[str] = None,
-        format: str = "rtx",
-        max_episodes_per_file: int = 1,
-        version: str = "0.0.1",
-        obs_keys=[],
-        act_keys=[],
-        step_keys=[],
-    ) -> None:
-        """
-        Export the dataset.
-
-        Args:
-            export_path (optional str): location of exported data. Uses dataset.path/export by default.
-            format (str): Supported formats are `rtx`, `open-x`, and `rlds`.
-        """
-        if format == "rtx" or format == "open-x" or format == "rlds":
-            self.export_rtx(export_path, max_episodes_per_file, version, obs_keys, act_keys, step_keys)
-        else:
-            raise ValueError("Unsupported export format")
-
-    def export_rtx(        
-        self,
-        export_path: Optional[str] = None,
-        max_episodes_per_file: int = 1,
-        version: str = "0.0.1",
-        obs_keys=[],
-        act_keys=[],
-        step_keys=[]
-    ):
-        if export_path == None:
-            export_path = self.path + "/export"
-        if not os.path.exists(export_path):
-            os.makedirs(export_path)
-
-        import dm_env
-        import tensorflow as tf
-        import tensorflow_datasets as tfds
-        from envlogger import step_data
-        from tensorflow_datasets.core.features import Tensor
-
-        from fog_x.rlds.writer import CloudBackendWriter
-
-        self.obs_keys += obs_keys
-        self.act_keys += act_keys
-        self.step_keys += step_keys
-
-        (
-            observation_tf_dict,
-            action_tf_dict,
-            step_tf_dict,
-        ) = self._get_tf_feature_dicts(
-            self.obs_keys,
-            self.act_keys,
-            self.step_keys,
-        )
-
-        logger.info("Exporting dataset as RT-X format")
-        logger.info(f"Observation keys: {observation_tf_dict}")
-        logger.info(f"Action keys: {action_tf_dict}")
-        logger.info(f"Step keys: {step_tf_dict}")
-
-        # generate tensorflow configuration file
-        ds_config = tfds.rlds.rlds_base.DatasetConfig(
-            name=self.name,
-            description="",
-            homepage="",
-            citation="",
-            version=tfds.core.Version("0.0.1"),
-            release_notes={
-                "0.0.1": "Initial release.",
-            },
-            observation_info=observation_tf_dict,
-            action_info=action_tf_dict,
-            reward_info=(
-                step_tf_dict["reward"]
-                if "reward" in step_tf_dict
-                else Tensor(shape=(), dtype=tf.float32)
-            ),
-            discount_info=(
-                step_tf_dict["discount"]
-                if "discount" in step_tf_dict
-                else Tensor(shape=(), dtype=tf.float32)
-            ),
-        )
-
-        ds_identity = tfds.core.dataset_info.DatasetIdentity(
-            name=ds_config.name,
-            version=tfds.core.Version(version),
-            data_dir=export_path,
-            module_name="",
-        )
-        writer = CloudBackendWriter(
-            data_directory=export_path,
-            ds_config=ds_config,
-            ds_identity=ds_identity,
-            max_episodes_per_file=max_episodes_per_file,
-        )
-
-        # export the dataset
-        episodes = self.get_episodes_from_metadata()
-        for episode in episodes:
-            steps = episode.collect().rows(named=True)
-            for i in range(len(steps)):
-                step = steps[i]
-                observationd = {}
-                actiond = {}
-                stepd = {}
-                for k, v in step.items():
-                    # logger.info(f"key: {k}")
-                    if k not in self.features:
-                        if k != "episode_id" and k != "Timestamp":
-                            logger.info(
-                                f"Feature {k} not found in the dataset features."
-                            )
-                        continue
-                    feature_spec = self.features[k].to_tf_feature_type()
-                    if (
-                        isinstance(feature_spec, tfds.core.features.Tensor)
-                        and feature_spec.shape != ()
-                    ):
-                        # reverse the process
-                        value = np.load(io.BytesIO(v)).astype(
-                            feature_spec.np_dtype
-                        )
-                    elif (
-                        isinstance(feature_spec, tfds.core.features.Tensor)
-                        and feature_spec.shape == ()
-                    ):
-                        value = np.array(v, dtype=feature_spec.np_dtype)
-                    elif isinstance(
-                        feature_spec, tfds.core.features.Image
-                    ):
-                        value = np.load(io.BytesIO(v)).astype(
-                            feature_spec.np_dtype
-                        )
-                    else:
-                        value = v
-
-                    if k in self.obs_keys:
-                        observationd[k] = value
-                    elif k in self.act_keys:
-                        actiond[k] = value
-                    else:
-                        stepd[k] = value
-
-                # logger.info(
-                #     f"Step: {stepd}"
-                #     f"Observation: {observationd}"
-                #     f"Action: {actiond}"
-                # )
-                timestep = dm_env.TimeStep(
-                    step_type=dm_env.StepType.FIRST,
-                    reward=np.float32(
-                        0.0
-                    ),  # stepd["reward"] if "reward" in step else np.float32(0.0),
-                    discount=np.float32(
-                        0.0
-                    ),  # stepd["discount"] if "discount" in step else np.float32(0.0),
-                    observation=observationd,
-                )
-                stepdata = step_data.StepData(
-                    timestep=timestep, action=actiond, custom_data=None
-                )
-                if i < len(steps) - 1:
-                    writer._record_step(stepdata, is_new_episode=False)
-                else:
-                    writer._record_step(stepdata, is_new_episode=True)
-
-
-    def load_rtx_episodes(
-        self,
-        name: str,
-        split: str = "all",
-        additional_metadata: Optional[Dict[str, Any]] = dict(),
-    ):
-        """
-        Load robot data from Tensorflow Datasets.
-
-        Args:
-            name (str): Name of RT-X episodes, which can be found at [Tensorflow Datasets](https://www.tensorflow.org/datasets/catalog) under the Robotics category
-            split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits)
-            additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes
-
-        Example:
-            ```
-            >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5)
-            >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"})
-            ```
-        """
-
-        # this is only required if rtx format is used
-        import tensorflow_datasets as tfds
-
-        from fog_x.rlds.utils import dataset2path
-        b = tfds.builder_from_directory(builder_dir=dataset2path(name))
-        self._build_rtx_episodes_from_tfds_builder(
-            b,
-            split=split,
-            additional_metadata=additional_metadata,
-        )
-
-    def load_rtx_episodes_local(
-        self,
-        path: str,
-        split: str = "all",
-        additional_metadata: Optional[Dict[str, Any]] = dict(),
-    ):
-        """
-        Load robot data from Tensorflow Datasets.
-
-        Args:
-            path (str): Path to the RT-X episodes
-            split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits)
-            additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes
-
-        Example:
-            ```
-            >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5")
-            >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"})
-            ```
-        """
-
-        # this is only required if rtx format is used
-        import tensorflow_datasets as tfds
-
-        b = tfds.builder_from_directory(path)
-        self._build_rtx_episodes_from_tfds_builder(
-            b,
-            split=split,
-            additional_metadata=additional_metadata,
-        )
-
-    def _build_rtx_episodes_from_tfds_builder(
-        self,
-        builder,
-        split: str = "all",
-        additional_metadata: Optional[Dict[str, Any]] = dict(),
-    ):
-        """
-        construct the dataset from the tfds builder
-        """
-        ds = builder.as_dataset(split=split)
-
-        data_type = builder.info.features["steps"]
-
-        for tf_episode in ds:
-            logger.info(tf_episode)
-            fog_episode = self.new_episode(
-                    metadata=additional_metadata,
-                )
-            for step in tf_episode["steps"]:
-                ret = self._load_rtx_step_data_from_tf_step(
-                    step, data_type, 
-                )
-                for r in ret:
-                    fog_episode.add(**r)
-
-            fog_episode.close()
-
-
-    def _prepare_rtx_metadata(
-        self,
-        name: str,
-        export_path: Optional[str] = None,
-        sample_size = 20,
-        shuffle = False,
-        seed = 42,
-    ):
-
-        # this is only required if rtx format is used
-        import tensorflow_datasets as tfds
-        from fog_x.rlds.utils import dataset2path
-        import cv2
-
-        b = tfds.builder_from_directory(builder_dir=dataset2path(name))
-        ds = b.as_dataset(split="all")
+        self.split = split
+        self.format = format
+        self.shuffle = shuffle
         if shuffle:
-            ds = ds.shuffle(sample_size, seed=seed)
-        data_type = b.info.features["steps"]
-        counter = 0
-
-        if export_path == None:
-            export_path = self.path + "/" + self.name + "_viz"
-        if not os.path.exists(export_path):
-            os.makedirs(export_path)
-
-        
-        for tf_episode in ds:
-            video_writers = {}
-
-            additional_metadata = {
-                "load_from": name,
-                "load_index": f"all, {shuffle}, {seed}, {counter}",
-            }
-            
-            logger.info(tf_episode)
-            fog_episode = self.new_episode()
-            
-            for step in tf_episode["steps"]:
-                ret = self._load_rtx_step_data_from_tf_step(
-                    step, data_type, 
-                )
-
-                for r in ret:
-                    feature_name = r["feature"]
-                    if "image" in feature_name and "depth" not in feature_name:
-                        image = np.load(io.BytesIO(r["value"]))
-
-                        # convert from RGB to BGR
-                        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-                
-                        if feature_name not in video_writers:
-                            
-                            output_filename = f"{self.name}_{counter}_{feature_name}"
-                            tmp_vid_output_path = f"/tmp/{output_filename}.mp4"
-                            output_path = f"{export_path}/{output_filename}"
-
-                            frame_size = (image.shape[1], image.shape[0])
-
-                            # save the initial image
-                            cv2.imwrite(f"{output_path}.jpg", image)
-                            # save the video
-                            video_writers[feature_name] = cv2.VideoWriter(
-                                tmp_vid_output_path,
-                                cv2.VideoWriter_fourcc(*"mp4v"),
-                                10,
-                                frame_size
-                            )
-                            
-
-                        video_writers[r["feature"]].write(image)
-
-                    if "instruction" in r["feature"]:
-                        natural_language_instruction = r["value"].decode("utf-8")
-                        additional_metadata["natural_language_instruction"] = natural_language_instruction
-
-                    r["metadata_only"] = True
-                    fog_episode.add(**r)
-            
-            for feature_name, video_writer in video_writers.items():
-                video_writer.release()
-                # need to convert to h264 to properly display over chrome / vscode 
-                output_filename = f"{self.name}_{counter}_{feature_name}"
-                tmp_vid_output_path = f"/tmp/{output_filename}.mp4"
-                vid_output_path = f"{export_path}/{output_filename}.mp4"
-                convert_to_h264(tmp_vid_output_path, vid_output_path)
-                additional_metadata[f"video_path_{feature_name}"] = output_filename
-                if os.path.isfile(tmp_vid_output_path):
-                    os.remove(tmp_vid_output_path)
-                
-            video_writers = {}
-            fog_episode.close(save_data = False, additional_metadata = additional_metadata)
-            counter += 1
-            if counter > sample_size:
-                break
-
-    def _load_rtx_step_data_from_tf_step(
-            self, 
-            step: Dict[str, Any],
-            data_type: Dict[str, Any] = {},
-    ): 
-        from tensorflow_datasets.core.features import (
-            FeaturesDict,
-            Image,
-            Scalar,
-            Tensor,
-            Text,
-        )
-        ret = []
-
-        for k, v in step.items():
-            # logger.info(f"k {k} , v {v}")
-            if isinstance(v, dict): #and (k == "observation" or k == "action"):
-                for k2, v2 in v.items():
-                    # TODO: abstract this to feature.py
-
-                    if (
-                        isinstance(data_type[k][k2], Tensor)
-                        and data_type[k][k2].shape != ()
-                    ):
-                        memfile = io.BytesIO()
-                        np.save(memfile, v2.numpy())
-                        value = memfile.getvalue()
-                    elif isinstance(data_type[k][k2], Image):
-                        memfile = io.BytesIO()
-                        np.save(memfile, v2.numpy())
-                        value = memfile.getvalue()
-                    else:
-                        value = v2.numpy()
-                    
-                    ret.append(
-                        {
-                            "feature": str(k2),
-                            "value": value,
-                            "feature_type": FeatureType(
-                                tf_feature_spec=data_type[k][k2]
-                            ),
-                        }
-                    )
-                    # fog_episode.add(
-                    #     feature=str(k2),
-                    #     value=value,
-                    #     feature_type=FeatureType(
-                    #         tf_feature_spec=data_type[k][k2]
-                    #     ),
-                    # )
-                    if k == "observation":
-                        self.obs_keys.append(k2)
-                    elif k == "action":
-                        self.act_keys.append(k2)
-            else:
-                # fog_episode.add(
-                #     feature=str(k),
-                #     value=v.numpy(),
-                #     feature_type=FeatureType(tf_feature_spec=data_type[k]),
-                # )
-                ret.append(
-                    {
-                        "feature": str(k),
-                        "value": v.numpy(),
-                        "feature_type": FeatureType(
-                            tf_feature_spec=data_type[k]
-                        ),
-                    }
-                )
-                self.step_keys.append(k)
-        return ret
-        
-
-    def get_episode_info(self) -> pandas.DataFrame:
-        """
-        Returns:
-            metadata of all episodes as `pandas.DataFrame`
-        """
-        return self.db_manager.get_episode_info_table()
-
-    def get_step_data(self) -> polars.LazyFrame:
-        """
-        Returns:
-            step data of all episodes
-        """
-        return self.db_manager.get_step_table_all()
-
-    def get_step_data_by_episode_ids(
-        self, episode_ids: List[int], as_lazy_frame=True
-    ):
-        """
-        Args:
-            episode_ids (List[int]): list of episode ids
-            as_lazy_frame (bool): whether to return polars.LazyFrame or polars.DataFrame
-
-        Returns:
-            step data of each episode
-        """
-        episodes = []
-        for episode_id in episode_ids:
-            if episode_id == None:
-                continue
-            if as_lazy_frame:
-                episodes.append(self.db_manager.get_step_table(episode_id))
-            else:
-                episodes.append(self.db_manager.get_step_table(episode_id).collect())
-        return episodes
-
-    def read_by(self, episode_info: Any = None) -> List[polars.LazyFrame]:
-        """
-        To be used with `Dataset.get_episode_info`.
-
-        Args:
-            episode_info (pandas.DataFrame): episode metadata information to determine which episodes to read
-
-        Returns:
-            episodes filtered by `episode_info`
-        """
-        episode_ids = list(episode_info["episode_id"])
-        logger.info(f"Reading episodes as order: {episode_ids}")
-        episodes = []
-        for episode_id in episode_ids:
-            if episode_id == None:
-                continue
-            episodes.append(self.db_manager.get_step_table(episode_id))
-        return episodes
-
-    def get_episodes_from_metadata(self, metadata: Any = None):
-        # Assume we use get_metadata_as_pandas_df to retrieve episodes metadata
-        if metadata is None:
-            metadata_df = self.get_episode_info()
+            self.loader = VLALoader(path, batch_size=1, return_type="tensor", split=split)
         else:
-            metadata_df = metadata
-        episodes = self.read_by(metadata_df)
-        return episodes
-
-    def pytorch_dataset_builder(self, metadata=None, **kwargs):
-        """
-        Used for loading current dataset as a PyTorch dataset.
-        To be used with `torch.utils.data.DataLoader`.
-        """
-
-        import torch
-        from torch.utils.data import Dataset
-        episodes = self.get_episodes_from_metadata(metadata)
-
-        # Initialize the PyTorch dataset with the episodes and features
-        pytorch_dataset = PyTorchDataset(episodes, self.features)
+            self.loader = NonShuffleVLALoader(path, batch_size=1, return_type="tensor")
+    
+    def __iter__(self):
+        return self
 
-        return pytorch_dataset
+    def __next__(self):
+        return self.loader.get_batch()[0]
 
-    def get_as_huggingface_dataset(self):
-        """
-        Load current dataset as a HuggingFace dataset.
+    def __len__(self):
+        raise NotImplementedError
 
-        TODO:
-            * currently the support for huggingg face dataset is limited.
-                it only shows its capability of easily returning a hf dataset
-            * add features from the episode metadata
-            * allow selecting episodes based on queries.
-                doing so requires creating a new copy of the dataset on disk
-        """
-        import datasets
+    def __getitem__(self, index):
+        raise NotImplementedError
 
-        dataset_path = self.path + "/" + self.name
-        parquet_files = [
-            os.path.join(dataset_path, f) for f in os.listdir(dataset_path)
-        ]
+    def get_tf_schema(self):
+        data = self.loader.peek()
+        return data_to_tf_schema(data)
 
-        hf_dataset = datasets.load_dataset("parquet", data_files=parquet_files)
-        return hf_dataset
+    def get_loader(self):
+        return self.loader
     
-class PyTorchDataset(Dataset):
-    def __init__(self, episodes, features):
-        """
-        Initialize the dataset with the episodes and features.
-        :param episodes: A list of episodes loaded from the database.
-        :param features: A dictionary of features to be included in the dataset.
-        """
-        self.episodes = episodes
-        self.features = features
-
-    def __len__(self):
-        """
-        Return the total number of episodes in the dataset.
-        """
-        return len(self.episodes)
-
-    def __getitem__(self, idx):
-        """
-        Retrieve the idx-th episode from the dataset.
-        Depending on the structure, you may need to process the episode
-        and its features here.
-        """
-        print("Retrieving episode at index", idx)
-        episode = self.episodes[idx].collect().to_pandas()
-        # Process the episode and its features here
-        # For simplicity, let's assume we're just returning the episode
-        return episode
+    def get_next_trajectory(self):
+        if self.shuffle:
+            return self.loader.peak(np.random.randint(0, len(self.loader))).load()
+        else:
+            return next(self.loader).load()
\ No newline at end of file
diff --git a/fog_x/deprecated/dataset.py b/fog_x/deprecated/dataset.py
new file mode 100644
index 0000000..f20d343
--- /dev/null
+++ b/fog_x/deprecated/dataset.py
@@ -0,0 +1,744 @@
+import io
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple
+import subprocess
+import numpy as np
+import polars
+import pandas
+
+from fog_x.database import (
+    DatabaseConnector,
+    DatabaseManager,
+    DataFrameConnector,
+    LazyFrameConnector,
+    PolarsConnector,
+)
+from fog_x.episode import Episode
+from fog_x.feature import FeatureType
+
+logger = logging.getLogger(__name__)
+
+
+
+def convert_to_h264(input_file, output_file):
+    
+    # FFmpeg command to convert video to H.264
+    command = [
+        'ffmpeg',
+        '-i', input_file,    # Input file
+        '-loglevel', 'error', # Suppress the logs
+        '-vcodec', 'h264', # Specify the codec
+        output_file          # Output file
+    ]
+    subprocess.run(command)
+
+def create_cloud_bucket_if_not_exist(provider, bucket_name, dir_name):
+    logger.info(f"Creating bucket '{bucket_name}' in cloud provider '{provider}' with folder '{dir_name}'...")
+    if provider == "s3":
+        import boto3
+        s3_client = boto3.client('s3')
+        # s3_client.create_bucket(Bucket=bucket_name)
+        s3_client.put_object(Bucket=bucket_name, Key=f"{dir_name}/")
+        logger.info(f"Bucket '{bucket_name}' created in AWS S3.")
+    elif provider == "gs":
+        from google.cloud import storage
+        """Create a folder in a Google Cloud Storage bucket if it does not exist."""
+        storage_client = storage.Client()
+        bucket = storage_client.bucket(bucket_name)
+
+        # Ensure the folder name ends with a '/'
+        if not dir_name.endswith('/'):
+            dir_name += '/'
+
+        # Check if folder exists by trying to list objects with the folder prefix
+        blobs = storage_client.list_blobs(bucket_name, prefix=dir_name, delimiter='/')
+        exists = any(blob.name == dir_name for blob in blobs)
+
+        if not exists:
+            # Create an empty blob to simulate a folder
+            blob = bucket.blob(dir_name)
+            blob.upload_from_string('')
+            print(f"Folder '{dir_name}' created.")
+        else:
+            print(f"Folder '{dir_name}' already exists.")
+    else:
+        raise ValueError(f"Unsupported cloud provider '{provider}'.")
+
+class Dataset:
+    """
+    Create or load from a new dataset.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        path: str = None,
+        replace_existing: bool = False,
+        features: Dict[
+            str, FeatureType
+        ] = {},  # features to be stored {name: FeatureType}
+        enable_feature_inference=True,  # whether additional features can be inferred
+        episode_info_connector: DatabaseConnector = None,
+        step_data_connector: DatabaseConnector = None,
+        storage: Optional[str] = None,
+    ) -> None:
+        """
+
+        Args:
+            name (str): Name of this dataset. Used as the directory name when exporting.
+            path (str): Required. Local path of where this dataset should be stored.
+            features (optional Dict[str, FeatureType]): Description of `param1`.
+            enable_feature_inference (bool): enable inferring additional FeatureTypes
+
+        Example:
+        ```
+        >>> dataset = fog_x.Dataset('my_dataset', path='~/fog_x/my_dataset`)
+        ```
+
+        TODO:
+            * is replace_existing actually used anywhere?
+        """
+        self.name = name
+
+        if path.startswith("."): # relative path
+            path = os.path.abspath(path).removesuffix("/")
+        elif path.startswith("~"): # home directory
+            path = os.path.expanduser(path).removesuffix("/")
+        elif path.startswith("/"): # absolute path
+            path = path.removesuffix("/")
+        elif path.startswith("s3://") or path.startswith("gs://"):
+            path = path.removesuffix("/")
+        else:
+            raise ValueError("Unsupported path format. Please use absolute path or relative path starting with '.' or '~'.")
+
+        logger.info(f"Dataset path: {path}")
+        self.path = path
+        if path is None:
+            raise ValueError("Path is required")
+        # create the folder if path doesn't exist
+        if self.path.startswith("/") and not os.path.exists(path):
+            logger.info(f"Creating directory {path}")
+            os.makedirs(path)
+
+        self.replace_existing = replace_existing
+        self.features = features
+        self.enable_feature_inference = enable_feature_inference
+        if episode_info_connector is None:
+                episode_info_connector = DataFrameConnector(f"{path}")
+        
+        if step_data_connector is None:
+            if self.path.startswith("/") and not os.path.exists(f"{path}/{name}"):
+                os.makedirs(f"{path}/{name}")
+            try: 
+                step_data_connector = LazyFrameConnector(f"{path}/{name}")
+            except:
+                logger.info(f"Path does not exist. ({path}/{name})")
+                cloud_provider = path[:2]
+                bucket_name = path[5:]
+                create_cloud_bucket_if_not_exist(cloud_provider, bucket_name, f"{name}/")
+                step_data_connector = LazyFrameConnector(f"{path}/{name}") 
+        self.db_manager = DatabaseManager(episode_info_connector, step_data_connector)
+        self.db_manager.initialize_dataset(self.name, features)
+
+        self.storage = storage
+        self.obs_keys = []
+        self.act_keys = []
+        self.step_keys = []
+
+    def new_episode(self, metadata: Optional[Dict[str, Any]] = None) -> Episode:
+        """
+        Create a new episode / trajectory.
+
+        Returns:
+            Episode
+
+        TODO:
+            * support multiple processes writing to the same episode
+            * close the previous episode if not closed
+        """
+        return Episode(
+            metadata=metadata,
+            features=self.features,
+            enable_feature_inference=self.enable_feature_inference,
+            db_manager=self.db_manager,
+        )
+
+    def _get_tf_feature_dicts(
+        self, obs_keys: List[str], act_keys: List[str], step_keys: List[str]
+    ) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
+        """
+        Get the tensorflow feature dictionaries.
+        """
+        observation_tf_dict = {}
+        action_tf_dict = {}
+        step_tf_dict = {}
+
+        for k in obs_keys:
+            observation_tf_dict[k] = self.features[k].to_tf_feature_type()
+
+        for k in act_keys:
+            action_tf_dict[k] = self.features[k].to_tf_feature_type()
+
+        for k in step_keys:
+            step_tf_dict[k] = self.features[k].to_tf_feature_type()
+
+        return observation_tf_dict, action_tf_dict, step_tf_dict
+
+    def export(
+        self,
+        export_path: Optional[str] = None,
+        format: str = "rtx",
+        max_episodes_per_file: int = 1,
+        version: str = "0.0.1",
+        obs_keys=[],
+        act_keys=[],
+        step_keys=[],
+    ) -> None:
+        """
+        Export the dataset.
+
+        Args:
+            export_path (optional str): location of exported data. Uses dataset.path/export by default.
+            format (str): Supported formats are `rtx`, `open-x`, and `rlds`.
+        """
+        if format == "rtx" or format == "open-x" or format == "rlds":
+            self.export_rtx(export_path, max_episodes_per_file, version, obs_keys, act_keys, step_keys)
+        else:
+            raise ValueError("Unsupported export format")
+
+    def export_rtx(        
+        self,
+        export_path: Optional[str] = None,
+        max_episodes_per_file: int = 1,
+        version: str = "0.0.1",
+        obs_keys=[],
+        act_keys=[],
+        step_keys=[]
+    ):
+        if export_path == None:
+            export_path = self.path + "/export"
+        if not os.path.exists(export_path):
+            os.makedirs(export_path)
+
+        import dm_env
+        import tensorflow as tf
+        import tensorflow_datasets as tfds
+        from envlogger import step_data
+        from tensorflow_datasets.core.features import Tensor
+
+        from fog_x.rlds.writer import CloudBackendWriter
+
+        self.obs_keys += obs_keys
+        self.act_keys += act_keys
+        self.step_keys += step_keys
+
+        (
+            observation_tf_dict,
+            action_tf_dict,
+            step_tf_dict,
+        ) = self._get_tf_feature_dicts(
+            self.obs_keys,
+            self.act_keys,
+            self.step_keys,
+        )
+
+        logger.info("Exporting dataset as RT-X format")
+        logger.info(f"Observation keys: {observation_tf_dict}")
+        logger.info(f"Action keys: {action_tf_dict}")
+        logger.info(f"Step keys: {step_tf_dict}")
+
+        # generate tensorflow configuration file
+        ds_config = tfds.rlds.rlds_base.DatasetConfig(
+            name=self.name,
+            description="",
+            homepage="",
+            citation="",
+            version=tfds.core.Version("0.0.1"),
+            release_notes={
+                "0.0.1": "Initial release.",
+            },
+            observation_info=observation_tf_dict,
+            action_info=action_tf_dict,
+            reward_info=(
+                step_tf_dict["reward"]
+                if "reward" in step_tf_dict
+                else Tensor(shape=(), dtype=tf.float32)
+            ),
+            discount_info=(
+                step_tf_dict["discount"]
+                if "discount" in step_tf_dict
+                else Tensor(shape=(), dtype=tf.float32)
+            ),
+        )
+
+        ds_identity = tfds.core.dataset_info.DatasetIdentity(
+            name=ds_config.name,
+            version=tfds.core.Version(version),
+            data_dir=export_path,
+            module_name="",
+        )
+        writer = CloudBackendWriter(
+            data_directory=export_path,
+            ds_config=ds_config,
+            ds_identity=ds_identity,
+            max_episodes_per_file=max_episodes_per_file,
+        )
+
+        # export the dataset
+        episodes = self.get_episodes_from_metadata()
+        for episode in episodes:
+            steps = episode.collect().rows(named=True)
+            for i in range(len(steps)):
+                step = steps[i]
+                observationd = {}
+                actiond = {}
+                stepd = {}
+                for k, v in step.items():
+                    # logger.info(f"key: {k}")
+                    if k not in self.features:
+                        if k != "episode_id" and k != "Timestamp":
+                            logger.info(
+                                f"Feature {k} not found in the dataset features."
+                            )
+                        continue
+                    feature_spec = self.features[k].to_tf_feature_type()
+                    if (
+                        isinstance(feature_spec, tfds.core.features.Tensor)
+                        and feature_spec.shape != ()
+                    ):
+                        # reverse the process
+                        value = np.load(io.BytesIO(v)).astype(
+                            feature_spec.np_dtype
+                        )
+                    elif (
+                        isinstance(feature_spec, tfds.core.features.Tensor)
+                        and feature_spec.shape == ()
+                    ):
+                        value = np.array(v, dtype=feature_spec.np_dtype)
+                    elif isinstance(
+                        feature_spec, tfds.core.features.Image
+                    ):
+                        value = np.load(io.BytesIO(v)).astype(
+                            feature_spec.np_dtype
+                        )
+                    else:
+                        value = v
+
+                    if k in self.obs_keys:
+                        observationd[k] = value
+                    elif k in self.act_keys:
+                        actiond[k] = value
+                    else:
+                        stepd[k] = value
+
+                # logger.info(
+                #     f"Step: {stepd}"
+                #     f"Observation: {observationd}"
+                #     f"Action: {actiond}"
+                # )
+                timestep = dm_env.TimeStep(
+                    step_type=dm_env.StepType.FIRST,
+                    reward=np.float32(
+                        0.0
+                    ),  # stepd["reward"] if "reward" in step else np.float32(0.0),
+                    discount=np.float32(
+                        0.0
+                    ),  # stepd["discount"] if "discount" in step else np.float32(0.0),
+                    observation=observationd,
+                )
+                stepdata = step_data.StepData(
+                    timestep=timestep, action=actiond, custom_data=None
+                )
+                if i < len(steps) - 1:
+                    writer._record_step(stepdata, is_new_episode=False)
+                else:
+                    writer._record_step(stepdata, is_new_episode=True)
+
+
+    def load_rtx_episodes(
+        self,
+        name: str,
+        split: str = "all",
+        additional_metadata: Optional[Dict[str, Any]] = dict(),
+    ):
+        """
+        Load robot data from Tensorflow Datasets.
+
+        Args:
+            name (str): Name of RT-X episodes, which can be found at [Tensorflow Datasets](https://www.tensorflow.org/datasets/catalog) under the Robotics category
+            split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits)
+            additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes
+
+        Example:
+            ```
+            >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5)
+            >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"})
+            ```
+        """
+
+        # this is only required if rtx format is used
+        import tensorflow_datasets as tfds
+
+        from fog_x.rlds.utils import dataset2path
+        b = tfds.builder_from_directory(builder_dir=dataset2path(name))
+        self._build_rtx_episodes_from_tfds_builder(
+            b,
+            split=split,
+            additional_metadata=additional_metadata,
+        )
+
+    def load_rtx_episodes_local(
+        self,
+        path: str,
+        split: str = "all",
+        additional_metadata: Optional[Dict[str, Any]] = dict(),
+    ):
+        """
+        Load robot data from Tensorflow Datasets.
+
+        Args:
+            path (str): Path to the RT-X episodes
+            split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits)
+            additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes
+
+        Example:
+            ```
+            >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5")
+            >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"})
+            ```
+        """
+
+        # this is only required if rtx format is used
+        import tensorflow_datasets as tfds
+
+        b = tfds.builder_from_directory(path)
+        self._build_rtx_episodes_from_tfds_builder(
+            b,
+            split=split,
+            additional_metadata=additional_metadata,
+        )
+
+    def _build_rtx_episodes_from_tfds_builder(
+        self,
+        builder,
+        split: str = "all",
+        additional_metadata: Optional[Dict[str, Any]] = dict(),
+    ):
+        """
+        construct the dataset from the tfds builder
+        """
+        ds = builder.as_dataset(split=split)
+
+        data_type = builder.info.features["steps"]
+
+        for tf_episode in ds:
+            logger.info(tf_episode)
+            fog_episode = self.new_episode(
+                    metadata=additional_metadata,
+                )
+            for step in tf_episode["steps"]:
+                ret = self._load_rtx_step_data_from_tf_step(
+                    step, data_type, 
+                )
+                for r in ret:
+                    fog_episode.add(**r)
+
+            fog_episode.close()
+
+
+    def _prepare_rtx_metadata(
+        self,
+        name: str,
+        export_path: Optional[str] = None,
+        sample_size = 20,
+        shuffle = False,
+        seed = 42,
+    ):
+
+        # this is only required if rtx format is used
+        import tensorflow_datasets as tfds
+        from fog_x.rlds.utils import dataset2path
+        import cv2
+
+        b = tfds.builder_from_directory(builder_dir=dataset2path(name))
+        ds = b.as_dataset(split="all")
+        if shuffle:
+            ds = ds.shuffle(sample_size, seed=seed)
+        data_type = b.info.features["steps"]
+        counter = 0
+
+        if export_path == None:
+            export_path = self.path + "/" + self.name + "_viz"
+        if not os.path.exists(export_path):
+            os.makedirs(export_path)
+
+        
+        for tf_episode in ds:
+            video_writers = {}
+
+            additional_metadata = {
+                "load_from": name,
+                "load_index": f"all, {shuffle}, {seed}, {counter}",
+            }
+            
+            logger.info(tf_episode)
+            fog_episode = self.new_episode()
+            
+            for step in tf_episode["steps"]:
+                ret = self._load_rtx_step_data_from_tf_step(
+                    step, data_type, 
+                )
+
+                for r in ret:
+                    feature_name = r["feature"]
+                    if "image" in feature_name and "depth" not in feature_name:
+                        image = np.load(io.BytesIO(r["value"]))
+
+                        # convert from RGB to BGR
+                        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+                
+                        if feature_name not in video_writers:
+                            
+                            output_filename = f"{self.name}_{counter}_{feature_name}"
+                            tmp_vid_output_path = f"/tmp/{output_filename}.mp4"
+                            output_path = f"{export_path}/{output_filename}"
+
+                            frame_size = (image.shape[1], image.shape[0])
+
+                            # save the initial image
+                            cv2.imwrite(f"{output_path}.jpg", image)
+                            # save the video
+                            video_writers[feature_name] = cv2.VideoWriter(
+                                tmp_vid_output_path,
+                                cv2.VideoWriter_fourcc(*"mp4v"),
+                                10,
+                                frame_size
+                            )
+                            
+
+                        video_writers[r["feature"]].write(image)
+
+                    if "instruction" in r["feature"]:
+                        natural_language_instruction = r["value"].decode("utf-8")
+                        additional_metadata["natural_language_instruction"] = natural_language_instruction
+
+                    r["metadata_only"] = True
+                    fog_episode.add(**r)
+            
+            for feature_name, video_writer in video_writers.items():
+                video_writer.release()
+                # need to convert to h264 to properly display over chrome / vscode 
+                output_filename = f"{self.name}_{counter}_{feature_name}"
+                tmp_vid_output_path = f"/tmp/{output_filename}.mp4"
+                vid_output_path = f"{export_path}/{output_filename}.mp4"
+                convert_to_h264(tmp_vid_output_path, vid_output_path)
+                additional_metadata[f"video_path_{feature_name}"] = output_filename
+                if os.path.isfile(tmp_vid_output_path):
+                    os.remove(tmp_vid_output_path)
+                
+            video_writers = {}
+            fog_episode.close(save_data = False, additional_metadata = additional_metadata)
+            counter += 1
+            if counter > sample_size:
+                break
+
+    def _load_rtx_step_data_from_tf_step(
+            self, 
+            step: Dict[str, Any],
+            data_type: Dict[str, Any] = {},
+    ): 
+        from tensorflow_datasets.core.features import (
+            FeaturesDict,
+            Image,
+            Scalar,
+            Tensor,
+            Text,
+        )
+        ret = []
+
+        for k, v in step.items():
+            # logger.info(f"k {k} , v {v}")
+            if isinstance(v, dict): #and (k == "observation" or k == "action"):
+                for k2, v2 in v.items():
+                    # TODO: abstract this to feature.py
+
+                    if (
+                        isinstance(data_type[k][k2], Tensor)
+                        and data_type[k][k2].shape != ()
+                    ):
+                        memfile = io.BytesIO()
+                        np.save(memfile, v2.numpy())
+                        value = memfile.getvalue()
+                    elif isinstance(data_type[k][k2], Image):
+                        memfile = io.BytesIO()
+                        np.save(memfile, v2.numpy())
+                        value = memfile.getvalue()
+                    else:
+                        value = v2.numpy()
+                    
+                    ret.append(
+                        {
+                            "feature": str(k2),
+                            "value": value,
+                            "feature_type": FeatureType(
+                                tf_feature_spec=data_type[k][k2]
+                            ),
+                        }
+                    )
+                    # fog_episode.add(
+                    #     feature=str(k2),
+                    #     value=value,
+                    #     feature_type=FeatureType(
+                    #         tf_feature_spec=data_type[k][k2]
+                    #     ),
+                    # )
+                    if k == "observation":
+                        self.obs_keys.append(k2)
+                    elif k == "action":
+                        self.act_keys.append(k2)
+            else:
+                # fog_episode.add(
+                #     feature=str(k),
+                #     value=v.numpy(),
+                #     feature_type=FeatureType(tf_feature_spec=data_type[k]),
+                # )
+                ret.append(
+                    {
+                        "feature": str(k),
+                        "value": v.numpy(),
+                        "feature_type": FeatureType(
+                            tf_feature_spec=data_type[k]
+                        ),
+                    }
+                )
+                self.step_keys.append(k)
+        return ret
+        
+
+    def get_episode_info(self) -> pandas.DataFrame:
+        """
+        Returns:
+            metadata of all episodes as `pandas.DataFrame`
+        """
+        return self.db_manager.get_episode_info_table()
+
+    def get_step_data(self) -> polars.LazyFrame:
+        """
+        Returns:
+            step data of all episodes
+        """
+        return self.db_manager.get_step_table_all()
+
+    def get_step_data_by_episode_ids(
+        self, episode_ids: List[int], as_lazy_frame=True
+    ):
+        """
+        Args:
+            episode_ids (List[int]): list of episode ids
+            as_lazy_frame (bool): whether to return polars.LazyFrame or polars.DataFrame
+
+        Returns:
+            step data of each episode
+        """
+        episodes = []
+        for episode_id in episode_ids:
+            if episode_id == None:
+                continue
+            if as_lazy_frame:
+                episodes.append(self.db_manager.get_step_table(episode_id))
+            else:
+                episodes.append(self.db_manager.get_step_table(episode_id).collect())
+        return episodes
+
+    def read_by(self, episode_info: Any = None) -> List[polars.LazyFrame]:
+        """
+        To be used with `Dataset.get_episode_info`.
+
+        Args:
+            episode_info (pandas.DataFrame): episode metadata information to determine which episodes to read
+
+        Returns:
+            episodes filtered by `episode_info`
+        """
+        episode_ids = list(episode_info["episode_id"])
+        logger.info(f"Reading episodes as order: {episode_ids}")
+        episodes = []
+        for episode_id in episode_ids:
+            if episode_id == None:
+                continue
+            episodes.append(self.db_manager.get_step_table(episode_id))
+        return episodes
+
+    def get_episodes_from_metadata(self, metadata: Any = None):
+        # Assume we use get_metadata_as_pandas_df to retrieve episodes metadata
+        if metadata is None:
+            metadata_df = self.get_episode_info()
+        else:
+            metadata_df = metadata
+        episodes = self.read_by(metadata_df)
+        return episodes
+
+    def pytorch_dataset_builder(self, metadata=None, **kwargs):
+        """
+        Used for loading current dataset as a PyTorch dataset.
+        To be used with `torch.utils.data.DataLoader`.
+        """
+
+        import torch
+        from torch.utils.data import Dataset
+        episodes = self.get_episodes_from_metadata(metadata)
+
+        # Initialize the PyTorch dataset with the episodes and features
+        pytorch_dataset = PyTorchDataset(episodes, self.features)
+
+        return pytorch_dataset
+
+    def get_as_huggingface_dataset(self):
+        """
+        Load current dataset as a HuggingFace dataset.
+
+        TODO:
+            * currently the support for huggingg face dataset is limited.
+                it only shows its capability of easily returning a hf dataset
+            * add features from the episode metadata
+            * allow selecting episodes based on queries.
+                doing so requires creating a new copy of the dataset on disk
+        """
+        import datasets
+
+        dataset_path = self.path + "/" + self.name
+        parquet_files = [
+            os.path.join(dataset_path, f) for f in os.listdir(dataset_path)
+        ]
+
+        hf_dataset = datasets.load_dataset("parquet", data_files=parquet_files)
+        return hf_dataset
+    
+class PyTorchDataset(Dataset):
+    def __init__(self, episodes, features):
+        """
+        Initialize the dataset with the episodes and features.
+        :param episodes: A list of episodes loaded from the database.
+        :param features: A dictionary of features to be included in the dataset.
+        """
+        self.episodes = episodes
+        self.features = features
+
+    def __len__(self):
+        """
+        Return the total number of episodes in the dataset.
+        """
+        return len(self.episodes)
+
+    def __getitem__(self, idx):
+        """
+        Retrieve the idx-th episode from the dataset.
+        Depending on the structure, you may need to process the episode
+        and its features here.
+        """
+        print("Retrieving episode at index", idx)
+        episode = self.episodes[idx].collect().to_pandas()
+        # Process the episode and its features here
+        # For simplicity, let's assume we're just returning the episode
+        return episode
diff --git a/fog_x/storage/__init__.py b/fog_x/deprecated/storage/__init__.py
similarity index 100%
rename from fog_x/storage/__init__.py
rename to fog_x/deprecated/storage/__init__.py
diff --git a/fog_x/storage/storage.py b/fog_x/deprecated/storage/storage.py
similarity index 100%
rename from fog_x/storage/storage.py
rename to fog_x/deprecated/storage/storage.py
diff --git a/fog_x/exporter/__init__.py b/fog_x/exporter/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fog_x/exporter/base.py b/fog_x/exporter/base.py
new file mode 100644
index 0000000..1afdd43
--- /dev/null
+++ b/fog_x/exporter/base.py
@@ -0,0 +1,10 @@
+
+from logging import getLogger
+
+class BaseExporter():
+    def __init__(self):
+        super(BaseExporter, self).__init__()
+        self.logger = getLogger(__name__)
+
+    def export(self, loader, path):
+        raise NotImplementedError
\ No newline at end of file
diff --git a/fog_x/feature.py b/fog_x/feature.py
index fa8d39f..fce4071 100644
--- a/fog_x/feature.py
+++ b/fog_x/feature.py
@@ -1,14 +1,10 @@
 import logging
-from typing import Any, List, Optional, Tuple
+from typing import Any, List, Optional, Tuple, Dict
 
 import numpy as np
-from sqlalchemy import Float, Integer, LargeBinary, String
-
-from fog_x.database.utils import type_np2sql, type_py2sql
 
 logger = logging.getLogger(__name__)
 
-
 SUPPORTED_DTYPES = [
     "null",
     "bool",
@@ -58,11 +54,11 @@ def __init__(
             self.from_tf_feature_type(tf_feature_spec)
         elif dtype is not None:
             self._set(dtype, shape)
-        else:
-            raise ValueError("Either dtype or data must be provided")
+
+
 
     def __str__(self):
-        return f"dtype={self.dtype}, shape={self.shape})"
+        return f"dtype={self.dtype}; shape={self.shape})"
 
     def __repr__(self):
         return self.__str__()
@@ -72,6 +68,8 @@ def _set(self, dtype: str, shape: Any):
             dtype = "float64"
         if dtype == "float":  # fix inferred type
             dtype = "float32"
+        if dtype == "object":
+            dtype = "string" 
         if dtype not in SUPPORTED_DTYPES:
             raise ValueError(f"Unsupported dtype: {dtype}")
         if shape is not None and not isinstance(shape, tuple):
@@ -112,23 +110,44 @@ def from_tf_feature_type(self, tf_feature_spec):
         self._set(str(dtype), shape)
         return self
 
+    @classmethod
     def from_data(self, data: Any):
         """
         Infer feature type from the provided data.
         """
+        feature_type = FeatureType()
         if isinstance(data, np.ndarray):
-            self._set(data.dtype.name, data.shape)
+            feature_type._set(data.dtype.name, data.shape)
+        elif isinstance(data, np.bool_):
+            feature_type._set("bool", ())
         elif isinstance(data, list):
             dtype = type(data[0]).__name__
             shape = (len(data),)
-            self._set(dtype.name, shape)
+            feature_type._set(dtype.name, shape)
         else:
             dtype = type(data).__name__
             shape = ()
-            self._set(dtype, shape)
-        return self
+            try:
+                feature_type._set(dtype, shape)
+            except ValueError as e:
+                print(f"Error: {e}")
+                print(f"dtype: {dtype}")
+                print(f"shape: {shape}")
+                print(f"data: {data}")
+                raise e
+        return feature_type
+
+    @classmethod
+    def from_str(self, feature_str: str):
+        """
+        Parse a string representation of the feature type.
+        """
+        dtype, shape = feature_str.split(";")
+        dtype = dtype.split("=")[1]
+        shape = eval(shape.split("=")[1][:-1]) # strip brackets
+        return FeatureType(dtype=dtype, shape=shape)
 
-    def to_tf_feature_type(self):
+    def to_tf_feature_type(self, first_dim_none=False):
         """
         Convert to tf feature
         """
@@ -164,22 +183,14 @@ def to_tf_feature_type(self):
             else:
                 return Scalar(dtype=tf_detype)
         elif len(self.shape) >= 1:
-            return Tensor(shape=self.shape, dtype=tf_detype)
+            if first_dim_none:
+                tf_shape = [None] + list(self.shape[1:])
+                return Tensor(shape=tf_shape, dtype=tf_detype)
+            else:
+                return Tensor(shape=self.shape, dtype=tf_detype)
         else:
             raise ValueError(f"Unsupported conversion to tf feature: {self}")
 
-    def to_sql_type(self):
-        """
-        Convert to sql type
-        """
-        if self.is_np:
-            return LargeBinary
-        else:
-            try:
-                return type_np2sql(self.dtype)
-            except:
-                return LargeBinary
-
     def to_pld_storage_type(self):
         if len(self.shape) == 0:
             if self.dtype == "string":
@@ -188,3 +199,5 @@ def to_pld_storage_type(self):
                 return self.dtype
         else:
             return "large_binary"
+
+
diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py
new file mode 100644
index 0000000..da928ba
--- /dev/null
+++ b/fog_x/loader/__init__.py
@@ -0,0 +1,4 @@
+from .base import BaseLoader
+from .rlds import RLDSLoader
+from .hdf5 import HDF5Loader
+from .vla import VLALoader, NonShuffleVLALoader
\ No newline at end of file
diff --git a/fog_x/loader/base.py b/fog_x/loader/base.py
new file mode 100644
index 0000000..c8c87e4
--- /dev/null
+++ b/fog_x/loader/base.py
@@ -0,0 +1,18 @@
+from logging import getLogger
+
+
+class BaseLoader():
+    def __init__(self, 
+                 path):
+        super(BaseLoader, self).__init__()
+        self.logger = getLogger(__name__)
+        self.path = path
+
+    # def get_schema(self) -> Schema:
+    #     raise NotImplementedError
+    
+    def __len__(self):
+        raise NotImplementedError
+
+    def __iter___(self):
+        raise NotImplementedError
diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py
new file mode 100644
index 0000000..4bfab81
--- /dev/null
+++ b/fog_x/loader/hdf5.py
@@ -0,0 +1,131 @@
+import torch
+from torch.utils.data import IterableDataset, DataLoader
+from . import BaseLoader
+import numpy as np
+import glob
+import h5py
+import asyncio
+import random
+import multiprocessing as mp
+import time
+import logging
+from fog_x.utils import _flatten, recursively_read_hdf5_group
+
+class HDF5Loader(BaseLoader):
+    def __init__(self, path, batch_size=1, buffer_size=50, num_workers=4):
+        super(HDF5Loader, self).__init__(path)
+        self.files = glob.glob(self.path, recursive=True)
+        self.batch_size = batch_size
+        self.buffer_size = buffer_size
+        self.buffer = mp.Queue(maxsize=buffer_size)
+        self.num_workers = num_workers
+        self.processes = []
+        random.shuffle(self.files)
+        self._start_workers()
+
+    def _worker(self):
+        while True:
+            if not self.files:
+                logging.info("Worker finished")
+                break
+            file_path = random.choice(self.files)
+            data = self._read_hdf5(file_path)
+            self.buffer.put(data)
+
+    def _start_workers(self):
+        for _ in range(self.num_workers):
+            p = mp.Process(target=self._worker)
+            p.start()
+            logging.debug(f"Started worker {p.pid}")
+            self.processes.append(p)
+
+    def get_batch(self):
+        batch = []
+        timeout = 5
+        start_time = time.time()
+
+        while len(batch) < self.batch_size:
+            if time.time() - start_time > timeout:
+                logging.warning(
+                    f"Timeout reached while getting batch. Batch size: {len(batch)}"
+                )
+                break
+
+            try:
+                item = self.buffer.get(timeout=1)
+                batch.append(item)
+            except mp.queues.Empty:
+                if (
+                    all(not p.is_alive() for p in self.processes)
+                    and self.buffer.empty()
+                ):
+                    if len(batch) == 0:
+                        return None
+                    else:
+                        break
+        return batch
+
+    def __next__(self):
+        batch = self.get_batch()
+        if batch is None:
+            random.shuffle(self.files)
+            self._start_workers()
+            raise StopIteration
+        return batch
+
+    def _read_hdf5(self, data_path):
+        with h5py.File(data_path, "r") as f:
+            data_unflattened = recursively_read_hdf5_group(f)
+        print(data_unflattened.keys())
+        data = {}
+        data["observation"] = _flatten(data_unflattened["observation"])
+        data["action"] = _flatten(data_unflattened["action"])
+
+        return data_unflattened
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self.files)
+
+    def peek(self):
+        if self.buffer.empty():
+            return None
+        return self.buffer.get()
+
+    def __del__(self):
+        for p in self.processes:
+            p.terminate()
+            p.join()
+
+
+class HDF5IterableDataset(IterableDataset):
+    def __init__(self, path, batch_size=1):
+        # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader
+        self.hdf5_loader = HDF5Loader(path, 1)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            batch = next(self.hdf5_loader)
+            return batch[0]  # Return a single item, not a batch
+        except StopIteration:
+            raise StopIteration
+
+
+def hdf5_collate_fn(batch):
+    # Convert data to PyTorch tensors
+    return batch
+
+
+def get_hdf5_dataloader(path: str, batch_size: int = 1, num_workers: int = 0):
+    dataset = HDF5IterableDataset(path, batch_size)
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=hdf5_collate_fn,
+        num_workers=num_workers,
+    )
diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py
new file mode 100644
index 0000000..8953fb5
--- /dev/null
+++ b/fog_x/loader/lerobot.py
@@ -0,0 +1,54 @@
+from . import BaseLoader
+import numpy as np
+import torch
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+class LeRobotLoader(BaseLoader):
+    def __init__(self, path, dataset_name, batch_size=1, delta_timestamps=None):
+        super(LeRobotLoader, self).__init__(path)
+        self.batch_size = batch_size
+        self.dataset = LeRobotDataset(root="/mnt/data/fog_x/hf/", repo_id=dataset_name, delta_timestamps=delta_timestamps)
+        self.episode_index = 0
+
+    def __len__(self):
+        return len(self.dataset.episode_data_index["from"])
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        max_retries = 3
+        batch_of_episodes = []
+
+        def _frame_to_numpy(frame):
+            return {k: np.array(v) for k, v in frame.items()}
+        for _ in range(self.batch_size):
+            episode = []
+            for attempt in range(max_retries):
+                try:
+                    # repeat
+                    if self.episode_index >= len(self.dataset):
+                        self.episode_index = 0
+                    try:
+                        from_idx = self.dataset.episode_data_index["from"][self.episode_index].item()
+                        to_idx = self.dataset.episode_data_index["to"][self.episode_index].item()
+                    except Exception as e:
+                        self.episode_index = 0
+                        continue
+                    frames = [_frame_to_numpy(self.dataset[idx]) for idx in range(from_idx, to_idx)]
+                    episode.extend(frames)
+                    self.episode_index += 1
+                    break
+                except Exception as e:
+                    if attempt == max_retries - 1:
+                        raise e
+                    self.episode_index += 1
+
+
+            batch_of_episodes.append((episode))
+            
+        
+        return batch_of_episodes
+
+    def get_batch(self):
+        return next(self)
diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py
new file mode 100644
index 0000000..9390308
--- /dev/null
+++ b/fog_x/loader/rlds.py
@@ -0,0 +1,78 @@
+from . import BaseLoader
+import numpy as np
+
+
+class RLDSLoader(BaseLoader):
+    def __init__(self, path, split, batch_size=1, shuffle_buffer=10, shuffling = True):
+        super(RLDSLoader, self).__init__(path)
+
+        try:
+            import tensorflow as tf
+            import tensorflow_datasets as tfds
+        except ImportError:
+            raise ImportError(
+                "Please install tensorflow and tensorflow_datasets to use rlds loader"
+            )
+
+        self.batch_size = batch_size
+        builder = tfds.builder_from_directory(path)
+        self.ds = builder.as_dataset(split)
+        self.length = len(self.ds)
+        self.shuffling = shuffling
+        if shuffling:
+            self.ds = self.ds.repeat()
+            self.ds = self.ds.shuffle(shuffle_buffer)
+        self.iterator = iter(self.ds)
+
+        self.split = split
+        self.index = 0
+
+    def __len__(self):
+        try:
+            import tensorflow as tf
+        except ImportError:
+            raise ImportError("Please install tensorflow to use rlds loader")
+
+        return self.length
+
+    def __iter__(self):
+        return self
+
+    def get_batch(self):
+        batch = self.ds.take(self.batch_size)
+        self.index += self.batch_size
+        if not self.shuffling and self.index >= self.length:
+            raise StopIteration
+        data = []
+        for b in batch:
+            data.append(self._convert_traj_to_numpy(b))
+        return data
+
+    def _convert_traj_to_numpy(self, traj):
+        import tensorflow as tf
+
+        def to_numpy(step_data):
+            step = {}
+            for key in step_data:
+                val = step_data[key]
+                if isinstance(val, dict):
+                    step[key] = {k: np.array(v) for k, v in val.items()}
+                else:
+                    step[key] = np.array(val)
+            return step
+
+        trajectory = []
+        for step in traj["steps"]:
+            trajectory.append(to_numpy(step))
+        return trajectory
+
+    def __next__(self):
+        data = [self._convert_traj_to_numpy(next(self.iterator))]
+        self.index += 1
+        if self.index >= self.length:
+            raise StopIteration
+        return data
+
+    def __getitem__(self, idx):
+        batch = next(iter(self.ds.skip(idx).take(1)))
+        return self._convert_traj_to_numpy(batch)
\ No newline at end of file
diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py
new file mode 100644
index 0000000..2db5ace
--- /dev/null
+++ b/fog_x/loader/vla.py
@@ -0,0 +1,237 @@
+from fog_x.loader.base import BaseLoader
+import fog_x
+import glob
+import logging
+import asyncio
+import os
+from typing import Text, List, Any
+import random
+from collections import deque
+import multiprocessing as mp
+import time
+from multiprocessing import Manager
+
+logger = logging.getLogger(__name__)
+
+class VLALoader:
+    def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", buffer_size=50, num_workers=-1, return_type = "numpy", split="all"):
+        self.files = self._get_files(path, split)
+        self.split = split
+        
+        self.cache_dir = cache_dir
+        self.batch_size = batch_size
+        self.return_type = return_type
+        # TODO: adjust buffer size
+        # if "autolab" in path:
+        #     self.buffer_size = 4
+        self.buffer_size = buffer_size
+        self.buffer = mp.Queue(maxsize=buffer_size)
+        if num_workers == -1:
+            num_workers = 2
+        self.num_workers = num_workers
+        self.processes = []
+        random.shuffle(self.files)
+        self._start_workers()
+        
+    def _get_files(self, path, split):
+        ret = []
+        if "*" in path:
+            ret = glob.glob(path)
+        elif os.path.isdir(path):
+            ret = glob.glob(os.path.join(path, "*.vla"))
+        else:
+            ret = [path]
+        if split == "train":
+            ret = ret[:int(len(ret)*0.9)]
+        elif split == "val":
+            ret = ret[int(len(ret)*0.9):]
+        elif split == "all":
+            pass
+        else:
+            raise ValueError(f"Invalid split: {split}")
+        return ret
+    
+    def _read_vla(self, data_path, return_type = None):
+        if return_type is None:
+            return_type = self.return_type
+        traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir)
+        ret = traj.load(return_type = return_type)
+        return ret
+
+    def _worker(self):
+        max_retries = 3
+        while True:
+            if not self.files:
+                logger.info("Worker finished")
+                break
+            
+            for attempt in range(max_retries):
+                try:
+                    file_path = random.choice(self.files)
+                    data = self._read_vla(file_path)
+                    self.buffer.put(data)
+                    break  # Exit the retry loop if successful
+                except Exception as e:
+                    logger.error(f"Error reading {file_path} on attempt {attempt + 1}: {e}")
+                    if attempt + 1 == max_retries:
+                        logger.error(f"Failed to read {file_path} after {max_retries} attempts")
+
+    def _start_workers(self):
+        for _ in range(self.num_workers):
+            p = mp.Process(target=self._worker)
+            p.start()
+            logger.debug(f"Started worker {p.pid}")
+            self.processes.append(p)
+
+    def get_batch(self) -> List[Any]:
+        batch = []
+        timeout = 5  # Adjust this value based on your needs
+        start_time = time.time()
+
+        while len(batch) < self.batch_size:
+            if time.time() - start_time > timeout:
+                logger.warning(f"Timeout reached while getting batch. Batch size: {len(batch)}")
+                break
+
+            try:
+                item = self.buffer.get(timeout=1)
+                batch.append(item)
+            except mp.queues.Empty:
+                if all(not p.is_alive() for p in self.processes) and self.buffer.empty():
+                    if len(batch) == 0:
+                        return None  # No more data available
+                    else:
+                        break  # Return partial batch
+
+        return batch
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        batch = self.get_batch()
+        if batch is None:
+            random.shuffle(self.files)
+            self._start_workers()
+            raise StopIteration
+        return batch
+
+    def __len__(self):
+        return len(self.files)
+
+    def peek(self):
+        file = random.choice(self.files)
+        return self._read_vla(file, return_type = "numpy")
+
+    def __del__(self):
+        for p in self.processes:
+            p.terminate()
+            p.join()
+            
+            
+class NonShuffleVLALoader:
+    def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", num_workers=1, return_type = "numpy"):
+        self.files = self._get_files(path)
+        self.cache_dir = cache_dir
+        self.batch_size = batch_size
+        self.return_type = return_type
+        self.index = 0
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= len(self.files):
+            raise StopIteration
+        
+        max_retries = 3
+        for attempt in range(max_retries):
+            try:
+                print(self.index)
+                file_path = self.files[self.index]
+                self.index += 1
+                return self._read_vla(file_path, return_type = self.return_type)
+            except Exception as e:
+                logger.error(f"Error reading {file_path} on attempt {attempt + 1}: {e}")
+                if attempt + 1 == max_retries:
+                    logger.error(f"Failed to read {file_path} after {max_retries} attempts")
+                    return None
+
+    def _get_files(self, path):
+        ret = []
+        if "*" in path:
+            ret = glob.glob(path)
+        elif os.path.isdir(path):
+            ret = glob.glob(os.path.join(path, "*.vla"))
+        else:
+            ret = [path]
+        # for file in ret:
+        #     try:
+        #         self._read_vla(file, return_type = self.return_type)
+        #     except Exception as e:
+        #         logger.error(f"Error reading {file}: {e}, ")
+        #         ret.remove(file)
+        return ret
+    
+    def __len__(self):
+        return len(self.files)
+    
+    def __getitem__(self, index):
+        return self.files[index]
+    
+    def __del__(self):
+        pass
+    
+    def peek(self):
+        file = self.files[self.index]
+        return self._read_vla(file, return_type = "numpy")
+
+    def _read_vla(self, data_path, return_type = None):
+        if return_type is None:
+            return_type = self.return_type
+        traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir)
+        ret = traj.load(return_type = return_type)
+        return ret
+    
+    def get_batch(self):
+        return [self.__next__() for _ in range(self.batch_size)]
+
+import torch
+from torch.utils.data import IterableDataset, DataLoader
+from fog_x.loader.vla import VLALoader
+from typing import Text, Optional
+
+class VLAIterableDataset(IterableDataset):
+    def __init__(self, path: Text, cache_dir: Optional[Text] = None, buffer_size: int = 1000):
+        # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader 
+        # in this case, we use pytorch dataloader for batching
+        self.vla_loader = VLALoader(path, batch_size=1, cache_dir=cache_dir, buffer_size=buffer_size)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        batch = self.vla_loader.get_batch()
+        if batch is None:
+            raise StopIteration
+        return batch[0]  # Return a single item, not a batch
+
+def vla_collate_fn(batch):
+    # Convert data to PyTorch tensors
+    # You may need to adjust this based on the structure of your VLA data
+    return batch #{k: torch.tensor(v) for k, v in batch[0].items()}
+
+def get_vla_dataloader(
+    path: Text,
+    batch_size: int = 1,
+    cache_dir: Optional[Text] = None,
+    buffer_size: int = 1000,
+    num_workers: int = 0
+):
+    dataset = VLAIterableDataset(path, cache_dir, buffer_size)
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=vla_collate_fn,
+        num_workers=num_workers
+    )
\ No newline at end of file
diff --git a/fog_x/rlds/__init__.py b/fog_x/rlds/__init__.py
deleted file mode 100644
index 5e0b1ef..0000000
--- a/fog_x/rlds/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from fog_x.rlds import utils
diff --git a/fog_x/rlds/utils.py b/fog_x/rlds/utils.py
deleted file mode 100644
index 11ad695..0000000
--- a/fog_x/rlds/utils.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-import tensorflow_datasets as tfds  # type: ignore
-from PIL import Image
-
-DATASETS = [
-    "fractal20220817_data",
-    "kuka",
-    "bridge",
-    "taco_play",
-    "jaco_play",
-    "berkeley_cable_routing",
-    "roboturk",
-    "nyu_door_opening_surprising_effectiveness",
-    "viola",
-    "berkeley_autolab_ur5",
-    "toto",
-    "language_table",
-    "columbia_cairlab_pusht_real",
-    "stanford_kuka_multimodal_dataset_converted_externally_to_rlds",
-    "nyu_rot_dataset_converted_externally_to_rlds",
-    "stanford_hydra_dataset_converted_externally_to_rlds",
-    "austin_buds_dataset_converted_externally_to_rlds",
-    "nyu_franka_play_dataset_converted_externally_to_rlds",
-    "maniskill_dataset_converted_externally_to_rlds",
-    "cmu_franka_exploration_dataset_converted_externally_to_rlds",
-    "ucsd_kitchen_dataset_converted_externally_to_rlds",
-    "ucsd_pick_and_place_dataset_converted_externally_to_rlds",
-    "austin_sailor_dataset_converted_externally_to_rlds",
-    "austin_sirius_dataset_converted_externally_to_rlds",
-    "bc_z",
-    "usc_cloth_sim_converted_externally_to_rlds",
-    "utokyo_pr2_opening_fridge_converted_externally_to_rlds",
-    "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds",
-    "utokyo_saytap_converted_externally_to_rlds",
-    "utokyo_xarm_pick_and_place_converted_externally_to_rlds",
-    "utokyo_xarm_bimanual_converted_externally_to_rlds",
-    "robo_net",
-    "berkeley_mvp_converted_externally_to_rlds",
-    "berkeley_rpt_converted_externally_to_rlds",
-    "kaist_nonprehensile_converted_externally_to_rlds",
-    "stanford_mask_vit_converted_externally_to_rlds",
-    "tokyo_u_lsmo_converted_externally_to_rlds",
-    "dlr_sara_pour_converted_externally_to_rlds",
-    "dlr_sara_grid_clamp_converted_externally_to_rlds",
-    "dlr_edan_shared_control_converted_externally_to_rlds",
-    "asu_table_top_converted_externally_to_rlds",
-    "stanford_robocook_converted_externally_to_rlds",
-    "eth_agent_affordances",
-    "imperialcollege_sawyer_wrist_cam",
-    "iamlab_cmu_pickup_insert_converted_externally_to_rlds",
-    "uiuc_d3field",
-    "utaustin_mutex",
-    "berkeley_fanuc_manipulation",
-    "cmu_play_fusion",
-    "cmu_stretch",
-    "berkeley_gnm_recon",
-    "berkeley_gnm_cory_hall",
-    "berkeley_gnm_sac_son",
-]
-
-
-def dataset2path(dataset_name):
-    if dataset_name == "robo_net":
-        version = "1.0.0"
-    elif dataset_name == "language_table":
-        version = "0.0.1"
-    else:
-        version = "0.1.0"
-    return f"gs://gresearch/robotics/{dataset_name}/{version}"
-
-
-def as_gif(images, path="temp.gif"):
-    # Render the images as the gif:
-    images[0].save(
-        path, save_all=True, append_images=images[1:], duration=1000, loop=0
-    )
-    gif_bytes = open(path, "rb").read()
-    return gif_bytes
-
-
-def get_dataset_info(datasets):
-    """
-    Get information about the datasets.
-
-    Args:
-      datasets (list): List of dataset names.
-
-    Returns:
-      list: List of tuples containing dataset name and dataset information.
-    """
-    ret = []
-    for name in datasets:
-        uri = dataset2path(name)
-        b = tfds.builder_from_directory(builder_dir=uri)
-        split = list(b.info.splits.keys())[0]
-        b.as_dataset(split=split)
-        ret.append((name, b.info))
-    return ret
diff --git a/fog_x/rlds/writer.py b/fog_x/rlds/writer.py
deleted file mode 100644
index 35ff9ea..0000000
--- a/fog_x/rlds/writer.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright 2022 The Regents of the University of California (Regents)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#         http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Copyright ©2022. The Regents of the University of California (Regents).
-# All Rights Reserved. Permission to use, copy, modify, and distribute this
-# software and its documentation for educational, research, and not-for-profit
-# purposes, without fee and without a signed licensing agreement, is hereby
-# granted, provided that the above copyright notice, this paragraph and the
-# following two paragraphs appear in all copies, modifications, and
-# distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150
-# Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643-7201,
-# otl@berkeley.edu, http://ipira.berkeley.edu/industry-info for commercial
-# licensing opportunities. IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY
-# FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES,
-# INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS
-# DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH
-# DAMAGE. REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-# PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY,
-# PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE
-# MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
-
-# coding=utf-8
-# Copyright 2023 DeepMind Technologies Limited..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#         http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""TFDS backend for Envlogger."""
-import dataclasses
-from collections import ChainMap
-from typing import Any, Dict, List, Optional
-
-import tensorflow_datasets as tfds
-from envlogger import step_data
-from envlogger.backends import backend_writer, rlds_utils
-
-DatasetConfig = tfds.rlds.rlds_base.DatasetConfig
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-@dataclasses.dataclass
-class Episode(object):
-    """Episode that is being constructed."""
-
-    prev_step: step_data.StepData
-    steps: Optional[List[rlds_utils.Step]] = None
-    metadata: Optional[Dict[str, Any]] = None
-
-    def add_step(self, step: step_data.StepData) -> None:
-        rlds_step = rlds_utils.to_rlds_step(self.prev_step, step)
-        if self.steps is None:
-            self.steps = []
-        self.steps.append(rlds_step)
-        self.prev_step = step
-
-    def get_rlds_episode(self) -> Dict[str, Any]:
-        last_step = rlds_utils.to_rlds_step(self.prev_step, None)
-        if self.steps is None:
-            self.steps = []
-        if self.metadata is None:
-            self.metadata = {}
-
-        return {"steps": self.steps + [last_step], **self.metadata}
-
-
-class CloudBackendWriter(backend_writer.BackendWriter):
-    """Backend that writes trajectory data in TFDS format (and RLDS structure)."""
-
-    def __init__(
-        self,
-        data_directory: str,
-        ds_config: tfds.rlds.rlds_base.DatasetConfig,
-        ds_identity: tfds.core.dataset_info.DatasetIdentity,
-        max_episodes_per_file: int = 1,
-        split_name: Optional[str] = None,
-        version: str = "0.0.1",
-        store_ds_metadata: bool = False,
-        **base_kwargs
-    ):
-        """Constructor.
-
-        Args:
-          data_directory: Directory to store the data
-          ds_config: Dataset Configuration.
-          max_episodes_per_file: Number of episodes to store per shard.
-          split_name: Name to be used by the split. If None, 'train' will be used.
-          version: version (major.minor.patch) of the dataset.
-          store_ds_metadata: if False, it won't store the dataset level
-            metadata.
-          **base_kwargs: arguments for the base class.
-        """
-        super().__init__(**base_kwargs)
-        if not split_name:
-            split_name = "train"
-        if store_ds_metadata:
-            metadata = self._metadata
-        else:
-            metadata = None
-        self._data_directory = data_directory
-        self._ds_info = tfds.rlds.rlds_base.build_info(
-            ds_config, ds_identity, metadata
-        )
-        self._ds_info.set_file_format("tfrecord")
-
-        self._current_episode = None
-
-        self._sequential_writer = tfds.core.SequentialWriter(
-            self._ds_info, max_episodes_per_file
-        )
-        self._split_name = split_name
-        self._sequential_writer.initialize_splits([split_name])
-        logging.info("self._data_directory: %r", self._data_directory)
-
-    def _write_and_reset_episode(self):
-        if self._current_episode is not None:
-            self._sequential_writer.add_examples(
-                {self._split_name: [self._current_episode.get_rlds_episode()]}
-            )
-            self._current_episode = None
-
-    def _record_step(
-        self, data: step_data.StepData, is_new_episode: bool
-    ) -> None:
-        """Stores RLDS steps in TFDS format."""
-
-        if is_new_episode:
-            self._write_and_reset_episode()
-
-        if self._current_episode is None:
-            self._current_episode = Episode(prev_step=data)
-        else:
-            self._current_episode.add_step(data)
-
-    def set_episode_metadata(self, data: Dict[str, Any]) -> None:
-        self._current_episode.metadata = data
-
-    def close(self) -> None:
-        logging.info(
-            "Deleting the backend with data_dir: %r", self._data_directory
-        )
-        self._write_and_reset_episode()
-        self._sequential_writer.close_all()
-        logging.info(
-            "Done deleting the backend with data_dir: %r", self._data_directory
-        )
diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py
new file mode 100644
index 0000000..da8f9d7
--- /dev/null
+++ b/fog_x/trajectory.py
@@ -0,0 +1,815 @@
+from fractions import Fraction
+import logging
+import time
+from typing import Any, Dict, List, Optional, Text
+import av
+import numpy as np
+import os
+from fog_x import FeatureType
+import pickle
+from fog_x.utils import recursively_read_hdf5_group
+import h5py
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+import sys
+
+logger = logging.getLogger(__name__)
+
+logging.getLogger("libav").setLevel(logging.CRITICAL)
+
+
+def _flatten_dict(d, parent_key="", sep="_"):
+    items = []
+    for k, v in d.items():
+        new_key = parent_key + sep + k if parent_key else k
+        if isinstance(v, dict):
+            items.extend(_flatten_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+
+class StreamInfo:
+    def __init__(self, feature_name, feature_type, encoding):
+        self.feature_name = feature_name
+        self.feature_type = feature_type
+        self.encoding = encoding
+
+    def __str__(self):
+        return f"StreamInfo({self.feature_name}, {self.feature_type}, {self.encoding})"
+
+    def __repr__(self):
+        return self.__str__()
+
+
+class Trajectory:
+    def __init__(
+        self,
+        path: Text,
+        mode="r",
+        cache_dir: Optional[Text] = "/tmp/fog_x/cache/",
+        lossy_compression: bool = True,
+        feature_name_separator: Text = "/",
+    ) -> None:
+        """
+        Args:
+            path (Text): path to the trajectory file
+            mode (Text, optional):  mode of the file, "r" for read and "w" for write
+            num_pre_initialized_h264_streams (int, optional):
+                Number of pre-initialized H.264 video streams to use when adding new features.
+                we pre initialize a configurable number of H.264 video streams to avoid the overhead of creating new streams for each feature.
+                otherwise we need to remux everytime
+            . Defaults to 5.
+            feature_name_separator (Text, optional):
+                Delimiter to separate feature names in the container file.
+                Defaults to "/".
+        """
+        self.path = path
+        self.feature_name_separator = feature_name_separator
+        # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache"
+        # use hex hash of the path for the cache file name
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir, exist_ok=True)
+        hex_hash = hex(abs(hash(self.path)))[2:]
+        self.cache_file_name = cache_dir + hex_hash + ".cache"
+        # self.cache_file_name = cache_dir + os.path.basename(self.path) + ".cache"
+        self.feature_name_to_stream = {}  # feature_name: stream
+        self.feature_name_to_feature_type = {}  # feature_name: feature_type
+        self.trajectory_data = None  # trajectory_data
+        self.start_time = time.time()
+        self.mode = mode
+        self.stream_id_to_info = {}  # stream_id: StreamInfo
+        self.is_closed = False
+        self.lossy_compression = lossy_compression
+        self.pending_write_tasks = []  # List to keep track of pending write tasks
+        # self.cache_write_lock = asyncio.Lock()
+        # self.cache_write_task = None
+        # self.executor = ThreadPoolExecutor(max_workers=1)
+
+        # check if the path exists
+        # if not, create a new file and start data collection
+        if self.mode == "w":
+            if not os.path.exists(self.path):
+                os.makedirs(os.path.dirname(self.path), exist_ok=True)
+            try:
+                self.container_file = av.open(self.path, mode="w", format="matroska")
+            except Exception as e:
+                logger.error(f"error creating the trajectory file: {e}")
+                raise
+        elif self.mode == "r":
+            if not os.path.exists(self.path):
+                raise FileNotFoundError(f"{self.path} does not exist")
+        else:
+            raise ValueError(f"Invalid mode {self.mode}, must be 'r' or 'w'")
+
+    def _get_current_timestamp(self):
+        current_time = (time.time() - self.start_time) * 1000
+        return current_time
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def __getitem__(self, key):
+        """
+        get the value of the feature
+        return hdf5-ed data
+        """
+
+        if self.trajectory_data is None:
+            logger.info(f"Loading the trajectory data with key {key}")
+            self.trajectory_data = self.load()
+
+        return self.trajectory_data[key]
+
+    def close(self, compact=True):
+        """
+        close the container file
+
+        args:
+        compact: re-read from the cache to encode pickled data to images
+        """
+        if self.is_closed:
+            raise ValueError("The container file is already closed")
+        try:
+            ts = self._get_current_timestamp()
+            for stream in self.container_file.streams:
+                try:
+                    packets = stream.encode(None)
+                    for packet in packets:
+                        packet.pts = ts
+                        packet.dts = ts
+                        self.container_file.mux(packet)
+                except Exception as e:
+                    logger.error(f"Error flushing stream {stream}: {e}")
+            logger.debug("Flushing the container file")
+        except av.error.EOFError:
+            pass  # This exception is expected and means the encoder is fully flushed
+
+        self.container_file.close()
+        if compact:
+            # After closing, re-read from the cache to encode pickled data to images
+            self._transcode_pickled_images(ending_timestamp=ts)
+        self.trajectory_data = None
+        self.container_file = None
+        self.is_closed = True
+
+    def load(self, save_to_cache=True, return_type="numpy"):
+        """
+        Load the trajectory data.
+
+        Args:
+            mode (str): "cache" to use cached data if available, "no_cache" to always load from container.
+            return_h5 (bool): If True, return h5py.File object instead of numpy arrays.
+
+        Returns:
+            dict: A dictionary of numpy arrays if return_h5 is False, otherwise an h5py.File object.
+        """
+
+        # uncomment the following line to use async
+        # return asyncio.get_event_loop().run_until_complete(
+        #     self.load_async(save_to_cache=save_to_cache, return_h5=return_h5)
+        # )
+        # async def load_async(self, save_to_cache=True, return_h5=False):
+        np_cache = None
+        if not os.path.exists(self.cache_file_name):
+            logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}")
+            np_cache = self._load_from_container()
+            if save_to_cache:
+                # await self._async_write_to_cache(np_cache)
+                try:
+                    self._write_to_cache(np_cache)
+                except Exception as e:
+                    logger.error(f"Error writing to cache file {self.cache_file_name}: {e}")
+                    return np_cache
+        
+        if return_type =="hdf5":
+            return h5py.File(self.cache_file_name, "r")
+        elif return_type == "numpy":
+            if not np_cache:
+                try:
+                    with h5py.File(self.cache_file_name, "r") as h5_cache:
+                        np_cache = recursively_read_hdf5_group(h5_cache)
+                except Exception as e:
+                    logger.error(f"Error loading cache file {self.cache_file_name}: {e}, reading from container")
+                    np_cache = self._load_from_container()
+            return np_cache
+        elif return_type == "cache_name":
+            return self.cache_file_name
+        elif return_type == "container":
+            return self.path
+        elif return_type == "tensor":
+            import tensorflow as tf
+            def _convert_h5_cache_to_tensor(h5_cache):
+                output_tf_traj = {}
+                for key in h5_cache:
+                    # hierarhical 
+                    if type(h5_cache[key]) == h5py._hl.group.Group:
+                        for sub_key in h5_cache[key]:
+                            if key not in output_tf_traj:
+                                output_tf_traj[key] = {}
+                            output_tf_traj[key][sub_key] = tf.convert_to_tensor(h5_cache[key][sub_key])
+                    elif type(h5_cache[key]) == h5py._hl.dataset.Dataset:
+                        output_tf_traj[key] = tf.convert_to_tensor(h5_cache[key])
+                return output_tf_traj
+            with h5py.File(self.cache_file_name, 'r') as h5_cache:
+                # Step 2: Access the dataset within the file
+                # Assume the dataset is named 'dataset_name'
+                output_traj = _convert_h5_cache_to_tensor(h5_cache)
+            return output_traj
+        else:
+            raise ValueError(f"Invalid return_type {return_type}")
+            
+            
+
+    def init_feature_streams(self, feature_spec: Dict):
+        """
+        initialize the feature stream with the feature name and its type
+        args:
+            feature_dict: dictionary of feature name and its type
+        """
+        for feature, feature_type in feature_spec.items():
+            encoding = self._get_encoding_of_feature(None, feature_type)
+            self.feature_name_to_stream[feature] = self._add_stream_to_container(
+                self.container_file, feature, encoding, feature_type
+            )
+
+    def add(
+        self,
+        feature: str,
+        data: Any,
+        timestamp: Optional[int] = None,
+    ) -> None:
+        """
+        add one value to container file
+
+        Args:
+            feature (str): name of the feature
+            value (Any): value associated with the feature; except dictionary
+            timestamp (optional int): nanoseconds since the Epoch.
+                If not provided, the current time is used.
+
+        Examples:
+            >>> trajectory.add('feature1', 'image1.jpg')
+
+        Logic:
+        - check the feature name
+        - if the feature name is not in the container, create a new stream
+
+        - check the type of value
+        - if value is numpy array, create a frame and encode it
+        - if it is a string or int, create a packet and encode it
+        - else raise an error
+
+        Exceptions:
+            raise an error if the value is a dictionary
+        """
+
+        if type(data) == dict:
+            raise ValueError("Use add_by_dict for dictionary")
+
+        feature_type = FeatureType.from_data(data)
+        # encoding = self._get_encoding_of_feature(data, None)
+        self.feature_name_to_feature_type[feature] = feature_type
+
+        # check if the feature is already in the container
+        # if not, create a new stream
+        # Check if the feature is already in the container
+        # here we enforce rawvideo encoding for all features
+        # later on the compacting step, we will encode the pickled data to images
+        if feature not in self.feature_name_to_stream:
+            self._on_new_stream(feature, "rawvideo", feature_type)
+
+        # get the stream
+        stream = self.feature_name_to_stream[feature]
+
+        # get the timestamp
+        if timestamp is None:
+            timestamp = self._get_current_timestamp()
+
+        # encode the frame
+        packets = self._encode_frame(data, stream, timestamp)
+
+        # write the packet to the container
+        for packet in packets:
+            self.container_file.mux(packet)
+
+    def add_by_dict(
+        self,
+        data: Dict[str, Any],
+        timestamp: Optional[int] = None,
+    ) -> None:
+        """
+        add one value to container file
+        data might be nested dictionary of values for each feature
+
+        Args:
+            data (Dict[str, Any]): dictionary of feature name and value
+            timestamp (optional int): nanoseconds since the Epoch.
+                If not provided, the current time is used.
+                assume the timestamp is same for all the features within the dictionary
+
+        Examples:
+            >>> trajectory.add_by_dict({'feature1': 'image1.jpg'})
+
+        Logic:
+        - check the data see if it is a dictionary
+        - if dictionary, need to flatten it and add each feature separately
+        """
+        if type(data) != dict:
+            raise ValueError("Use add for non-dictionary data, type is ", type(data))
+
+        _flatten_dict_data = _flatten_dict(data, sep=self.feature_name_separator)
+        timestamp = self._get_current_timestamp() if timestamp is None else timestamp
+        for feature, value in _flatten_dict_data.items():
+            self.add(feature, value, timestamp)
+
+    @classmethod
+    def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text, lossy_compression: bool = True) -> "Trajectory":
+        """
+        Create a Trajectory object from a list of dictionaries.
+
+        args:
+            data (List[Dict[str, Any]]): list of dictionaries
+            path (Text): path to the trajectory file
+
+        Example:
+        original_trajectory = [
+            {"feature1": "value1", "feature2": "value2"},
+            {"feature1": "value3", "feature2": "value4"},
+        ]
+
+        trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla")
+        """
+        traj = cls(path, mode="w", lossy_compression=lossy_compression)
+        logger.info(f"Creating a new trajectory file at {path} with {len(data)} steps")
+        for step in data:
+            traj.add_by_dict(step)
+        traj.close()
+        return traj
+
+    @classmethod
+    def from_dict_of_lists(
+        cls, data: Dict[str, List[Any]], path: Text, feature_name_separator: Text = "/", lossy_compression: bool = True
+    ) -> "Trajectory":
+        """
+        Create a Trajectory object from a dictionary of lists.
+
+        Args:
+            data (Dict[str, List[Any]]): dictionary of lists. Assume list length is the same for all features.
+            path (Text): path to the trajectory file
+
+        Returns:
+            Trajectory: _description_
+
+        Example:
+        original_trajectory = {
+            "feature1": ["value1", "value3"],
+            "feature2": ["value2", "value4"],
+        }
+
+        trajectory = Trajectory.from_dict_of_lists(original_trajectory, path="/tmp/fog_x/output.vla")
+        """
+        traj = cls(path, feature_name_separator=feature_name_separator, mode="w", lossy_compression = lossy_compression)
+        # flatten the data such that all data starts and put feature name with separator
+        _flatten_dict_data = _flatten_dict(data, sep=traj.feature_name_separator)
+
+        # Check if all lists have the same length
+        list_lengths = [len(v) for v in _flatten_dict_data.values()]
+        if len(set(list_lengths)) != 1:
+            raise ValueError(
+                "All lists must have the same length",
+                [(k, len(v)) for k, v in _flatten_dict_data.items()],
+            )
+
+        for i in range(list_lengths[0]):
+            step = {k: v[i] for k, v in _flatten_dict_data.items()}
+            traj.add_by_dict(step)
+        traj.close()
+        return traj
+
+    def _load_from_cache(self):
+        """
+        load the cached file with entire vla trajctory
+        """
+        h5_cache = h5py.File(self.cache_file_name, "r")
+        return h5_cache
+
+    def _load_from_container(self):
+        """
+        Load the container file with the entire VLA trajectory using multi-processing for image streams.
+        
+        args:
+            save_to_cache: save the decoded data to the cache file
+        
+        returns:
+            np_cache: dictionary with the decoded data
+
+        Workflow:
+        - Get schema of the container file.
+        - Preallocate decoded streams.
+        - Use multi-processing to decode image streams separately.
+        - Decode non-image streams in the main process.
+        - Combine results from all processes.
+        """
+
+        def _get_length_of_stream(container, stream):
+            """
+            Get the length of the stream.
+            """
+            length = 0
+            for packet in container.demux([stream]):
+                if packet.dts is not None:
+                    length += 1
+            return length
+        
+        container_to_get_length = av.open(self.path, mode="r", format="matroska")
+        streams = container_to_get_length.streams
+        length = _get_length_of_stream(container_to_get_length, streams[0])
+        logger.debug(f"Length of the stream is {length}")
+        container_to_get_length.close()
+        
+        container = av.open(self.path, mode="r", format="matroska")
+        streams = container.streams
+
+
+        # Dictionary to store preallocated numpy arrays
+        np_cache = {}
+        feature_name_to_stream = {}
+
+        # Preallocate memory for the streams in numpy arrays
+        for stream in streams:
+            feature_name = stream.metadata.get("FEATURE_NAME")
+            if feature_name is None:
+                logger.warn(f"Skipping stream without FEATURE_NAME: {stream}")
+                continue
+            feature_type = FeatureType.from_str(stream.metadata.get("FEATURE_TYPE"))
+            feature_name_to_stream[feature_name] = stream
+            self.feature_name_to_feature_type[feature_name] = feature_type
+
+            logger.debug(
+                f"Creating a cache for {feature_name} with shape {feature_type.shape}"
+            )
+
+            # Allocate numpy array with shape [None, X, Y, Z] where X, Y, Z are feature dimensions
+            if feature_type.dtype == "string":
+                np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object)
+            else:
+                np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype)
+
+        # Decode the frames and store them in the preallocated numpy memory
+        d_feature_length = {feature: 0 for feature in feature_name_to_stream}
+        for packet in container.demux(list(streams)):
+            feature_name = packet.stream.metadata.get("FEATURE_NAME")
+            if feature_name is None:
+                logger.debug(f"Skipping stream without FEATURE_NAME: {packet.stream}")
+                continue
+            feature_type = FeatureType.from_str(packet.stream.metadata.get("FEATURE_TYPE"))
+
+            logger.debug(
+                f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}"
+            )
+
+            feature_codec = packet.stream.codec_context.codec.name
+            if feature_codec == "rawvideo":
+                packet_in_bytes = bytes(packet)
+                if packet_in_bytes:
+                    # Decode the packet
+                    data = pickle.loads(packet_in_bytes)
+
+                    # Append data to the numpy array
+                    np_cache[feature_name][d_feature_length[feature_name]] = data
+                    d_feature_length[feature_name] += 1
+                else:
+                    logger.debug(f"Skipping empty packet: {packet} for {feature_name}")
+            else:
+                frames = packet.decode()
+                for frame in frames:
+                    if feature_type.dtype == "float32":
+                        data = frame.to_ndarray(format="gray").reshape(feature_type.shape)
+                    else:
+                        data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape)
+                    # data = np.asarray(frame.to_image())#.reshape(feature_type.shape)
+                    # save the numpy to image folder
+                    # Append data to the numpy array
+                    np_cache[feature_name][d_feature_length[feature_name]] = data
+                    d_feature_length[feature_name] += 1
+
+        logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}")
+        container.close()
+
+        return np_cache
+
+    # async def _async_write_to_cache(self, np_cache):
+    #     async with self.cache_write_lock:
+    #         await asyncio.get_event_loop().run_in_executor(
+    #             self.executor,
+    #             self._write_to_cache,
+    #             np_cache
+    #         )
+
+    def _write_to_cache(self, np_cache):
+        try:
+            h5_cache = h5py.File(self.cache_file_name, "w")
+        except Exception as e:
+            logger.error(f"Error creating cache file: {e}")
+            raise
+        for feature_name, data in np_cache.items():
+            if data.dtype == object:
+                for i in range(len(data)):
+                    data_type = type(data[i])
+                    if data_type in (str, bytes, np.ndarray):
+                        data[i] = str(data[i])
+                    else:
+                        data[i] = str(data[i])
+                try:
+                    h5_cache.create_dataset(feature_name, data=data)
+                except Exception as e:
+                    logger.error(f"Error saving {feature_name} to cache: {e} with data {data}")
+            else:
+                h5_cache.create_dataset(feature_name, data=data)
+        h5_cache.close()
+                    
+    def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None):
+        """
+        Transcode pickled images into the desired format (e.g., raw or encoded images).
+        """
+
+        # Move the original file to a temporary location
+        temp_path = self.path + ".temp"
+        os.rename(self.path, temp_path)
+
+        # Open the original container for reading
+        original_container = av.open(temp_path, mode="r", format="matroska")
+        original_streams = list(original_container.streams)
+
+        # Create a new container
+        new_container = av.open(self.path, mode="w", format="matroska")
+
+        # Add existing streams to the new container
+        d_original_stream_id_to_new_container_stream = {}
+        for stream in original_streams:
+            stream_feature = stream.metadata.get("FEATURE_NAME")
+            if stream_feature is None:
+                logger.debug(f"Skipping stream without FEATURE_NAME: {stream}")
+                continue
+            # Determine encoding method based on feature type
+            stream_encoding = self._get_encoding_of_feature(
+                None, self.feature_name_to_feature_type[stream_feature]
+            )
+            stream_feature_type = self.feature_name_to_feature_type[stream_feature]
+            stream_in_updated_container = self._add_stream_to_container(
+                new_container, stream_feature, stream_encoding, stream_feature_type
+            )
+
+            # Preserve the stream metadata
+            for key, value in stream.metadata.items():
+                stream_in_updated_container.metadata[key] = value
+
+            d_original_stream_id_to_new_container_stream[stream.index] = (
+                stream_in_updated_container
+            )
+
+        # Initialize the number of packets per stream
+        # Transcode pickled images and add them to the new container
+        for packet in original_container.demux(original_streams):
+
+            def is_packet_valid(packet):
+                return packet.pts is not None and packet.dts is not None
+
+            if is_packet_valid(packet):
+                packet.stream = d_original_stream_id_to_new_container_stream[
+                    packet.stream.index
+                ]
+
+                # Check if the stream is using rawvideo, meaning it's a pickled stream
+                if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libaom-av1":
+                    data = pickle.loads(bytes(packet))
+
+                    # Encode the image data as needed, example shown for raw images
+                    new_packets = self._encode_frame(data, packet.stream, packet.pts)
+
+                    for new_packet in new_packets:
+                        new_container.mux(new_packet)
+                else:
+                    # If not a rawvideo stream, just remux the existing packet
+                    new_container.mux(packet)
+            else:
+                logger.debug(f"Skipping invalid packet: {packet}")
+
+        # flush the streams
+        for stream in new_container.streams:
+            packets = stream.encode(None)
+            for packet in packets:
+                packet.pts = ending_timestamp
+                packet.dts = ending_timestamp
+                new_container.mux(packet)
+
+        original_container.close()
+        os.remove(temp_path)
+
+        # Reopen the new container for further writing new data
+        self.container_file = new_container
+
+    def to_hdf5(self, path: Text):
+        """
+        convert the container file to hdf5 file
+        """
+
+        if not self.trajectory_data:
+            self.load()
+
+        # directly copy the cache file to the hdf5 file
+        os.rename(self.cache_file_name, path)
+
+    def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packet]:
+        """
+        encode the frame and write it to the stream file, return the packet
+        args:
+            data: data frame to be encoded
+            stream: stream to write the frame
+            timestamp: timestamp of the frame
+        return:
+            packet: encoded packet
+        """
+        encoding = stream.codec_context.codec.name
+        feature_type = FeatureType.from_data(data)
+        logger.debug(f"Encoding {stream.metadata.get('FEATURE_NAME')} with {encoding}")
+        if encoding == "ffv1" or encoding == "libaom-av1":
+            if feature_type.dtype == "float32":
+                frame = self._create_frame_depth(data, stream)
+            else:
+                frame = self._create_frame(data, stream)
+            frame.pts = timestamp
+            frame.dts = timestamp
+            frame.time_base = stream.time_base
+            packets = stream.encode(frame)
+        else:
+            packet = av.Packet(pickle.dumps(data))
+            packet.dts = timestamp
+            packet.pts = timestamp
+            packet.time_base = stream.time_base
+            packet.stream = stream
+
+            packets = [packet]
+
+        for packet in packets:
+            packet.pts = timestamp
+            packet.dts = timestamp
+            packet.time_base = stream.time_base
+        return packets
+
+    def _on_new_stream(self, new_feature, new_encoding, new_feature_type):
+        if new_feature in self.feature_name_to_stream:
+            return
+
+        if not self.feature_name_to_stream:
+            logger.debug(f"Creating a new stream for the first feature {new_feature}")
+            self.feature_name_to_stream[new_feature] = self._add_stream_to_container(
+                self.container_file, new_feature, new_encoding, new_feature_type
+            )
+        else:
+            logger.debug(f"Adding a new stream for the feature {new_feature}")
+            # Following is a workaround because we cannot add new streams to an existing container
+            # Close current container
+            self.close(compact=False)
+
+            # Move the original file to a temporary location
+            temp_path = self.path + ".temp"
+            os.rename(self.path, temp_path)
+
+            # Open the original container for reading
+            original_container = av.open(temp_path, mode="r", format="matroska")
+            original_streams = list(original_container.streams)
+
+            # Create a new container
+            new_container = av.open(self.path, mode="w", format="matroska")
+
+            # Add existing streams to the new container
+            d_original_stream_id_to_new_container_stream = {}
+            for stream in original_streams:
+                stream_feature = stream.metadata.get("FEATURE_NAME")
+                if stream_feature is None:
+                    logger.debug(f"Skipping stream without FEATURE_NAME: {stream}")
+                    continue
+                stream_encoding = stream.codec_context.codec.name
+                stream_feature_type = self.feature_name_to_feature_type[stream_feature]
+                stream_in_updated_container = self._add_stream_to_container(
+                    new_container, stream_feature, stream_encoding, stream_feature_type
+                )
+                # new_stream.options = stream.options
+                for key, value in stream.metadata.items():
+                    stream_in_updated_container.metadata[key] = value
+                d_original_stream_id_to_new_container_stream[stream.index] = (
+                    stream_in_updated_container
+                )
+
+            # Add new feature stream
+            new_stream = self._add_stream_to_container(
+                new_container, new_feature, new_encoding, new_feature_type
+            )
+            d_original_stream_id_to_new_container_stream[new_stream.index] = new_stream
+            self.stream_id_to_info[new_stream.index] = StreamInfo(
+                new_feature, new_feature_type, new_encoding
+            )
+
+            # Remux existing packets
+            for packet in original_container.demux(original_streams):
+
+                def is_packet_valid(packet):
+                    return packet.pts is not None and packet.dts is not None
+
+                if is_packet_valid(packet):
+                    packet.stream = d_original_stream_id_to_new_container_stream[
+                        packet.stream.index
+                    ]
+                    new_container.mux(packet)
+                else:
+                    pass
+
+            original_container.close()
+            os.remove(temp_path)
+
+            # Reopen the new container for writing new data
+            self.container_file = new_container
+            self.feature_name_to_stream[new_feature] = new_stream
+            self.is_closed = False
+
+    def _add_stream_to_container(self, container, feature_name, encoding, feature_type):
+        stream = container.add_stream(encoding)
+        if encoding == "ffv1":
+            stream.width = feature_type.shape[1]
+            stream.height = feature_type.shape[0]
+            # stream.codec_context.options = {
+            #     "preset": "fast",  # Set preset to 'fast' for quicker encoding
+            #     "tune": "zerolatency",  # Reduce latency
+            # }
+        
+        if encoding == "libaom-av1":
+            stream.width = feature_type.shape[1]
+            stream.height = feature_type.shape[0]
+            stream.codec_context.options = {
+                "g": "2",
+                'crf': '30',  # Constant Rate Factor (quality)
+            }
+            # stream.codec_context.options = {
+            #     "preset": "ultrafast",  # Set preset to 'ultrafast' for quicker encoding
+            #     "tune": "zerolatency",  # Reduce latency
+            #     'crf': '30',  # Constant Rate Factor (quality)
+            # }
+
+        stream.metadata["FEATURE_NAME"] = feature_name
+        stream.metadata["FEATURE_TYPE"] = str(feature_type)
+        stream.time_base = Fraction(1, 1000)
+        return stream
+
+    def _create_frame(self, image_array, stream):
+        frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8))
+        frame.pict_type = "NONE"
+        return frame
+
+    def _create_frame_depth(self, image_array, stream):
+        image_array = np.array(image_array)
+        # if float, convert to uint8
+        # TODO: this is a hack, need to fix it
+        if image_array.dtype == np.float32:
+            image_array = (image_array * 255).astype(np.uint8)
+        # if 3 dim, convert to 2 dim
+        if len(image_array.shape) == 3:
+            image_array = image_array[:, :, 0]
+        frame = av.VideoFrame.from_ndarray(image_array, format="gray")
+        frame.pict_type = "NONE"
+        frame.time_base = stream.time_base
+        return frame
+
+    def _get_encoding_of_feature(
+        self, feature_value: Any, feature_type: Optional[FeatureType]
+    ) -> Text:
+        """
+        get the encoding of the feature value
+        args:
+            feature_value: value of the feature
+            feature_type: type of the feature
+        return:
+            encoding of the feature in string
+        """
+        if feature_type is None:
+            feature_type = FeatureType.from_data(feature_value)
+        data_shape = feature_type.shape
+        if len(data_shape) >= 2 and data_shape[0] >= 100 and data_shape[1] >= 100:
+            if self.lossy_compression:
+                vid_coding = "libaom-av1"
+            else:
+                vid_coding = "ffv1"
+        else:
+            vid_coding = "rawvideo"
+        return vid_coding
+
+    def save_stream_info(self):
+        # serialize and save the stream info
+        with open(self.path + ".stream_info", "wb") as f:
+            pickle.dump(self.stream_id_to_info, f)
+
+    def load_stream_info(self):
+        # load the stream info
+        with open(self.path + ".stream_info", "rb") as f:
+            self.stream_id_to_info = pickle.load(f)
diff --git a/fog_x/utils.py b/fog_x/utils.py
new file mode 100644
index 0000000..fdfba86
--- /dev/null
+++ b/fog_x/utils.py
@@ -0,0 +1,44 @@
+
+from typing import Any, Dict
+import numpy as np
+from fog_x.feature import FeatureType
+
+
+def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]:
+    """
+    Convert data to a tf schema
+    """
+    data = _flatten(data)
+    schema = {}
+    for k, v in data.items():
+        if "/" in k: # make the subkey to be within dict 
+            main_key, sub_key = k.split("/")
+            if main_key not in schema:
+                schema[main_key] = {}
+            schema[main_key][sub_key] = FeatureType.from_data(v).to_tf_feature_type(first_dim_none=True)
+            # replace first element of shape with None
+        else:
+            schema[k] = FeatureType.from_data(v).to_tf_feature_type(first_dim_none=True)
+    return schema
+
+
+# flatten the data such that all data starts with root level tree (observation and action)
+def _flatten(data, parent_key="", sep="/"):
+    items = {}
+    for k, v in data.items():
+        new_key = parent_key + sep + k if parent_key else k
+        if isinstance(v, dict):
+            items.update(_flatten(v, new_key, sep))
+        else:
+            items[new_key] = v
+    return items
+
+import h5py
+def recursively_read_hdf5_group(group):
+    if isinstance(group, h5py.Dataset):
+        return np.array(group)
+    elif isinstance(group, h5py.Group):
+        return {key: recursively_read_hdf5_group(value) for key, value in group.items()}
+    else:
+        raise TypeError("Unsupported HDF5 group type")
+
diff --git a/openx_to_vla.sh b/openx_to_vla.sh
new file mode 100755
index 0000000..ec1912c
--- /dev/null
+++ b/openx_to_vla.sh
@@ -0,0 +1,48 @@
+
+
+# # bridge dataset
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16  --lossless
+
+# berkeley_cable_routing dataset
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16  --lossless
+# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16
+
+# nyu_door_opening_surprising_effectiveness dataset
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16  --lossless
+# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16
+
+# bridge dataset
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[6000:] --max_workers 16
+# pkill -f examples
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless
+python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8
+pkill -f examples
+
+# berkeley_autolab_ur5 dataset
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:] --max_workers 16
+# pkill -f examples
+python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8
+pkill -f examples
+
+
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 16 
+
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16  --lossless
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 16 --lossless
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 16 --lossless
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 16 --lossless
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 16 --lossless
+
+
+# fractal20220817_data
+# rm -rf /home/kych/datasets/fractal20220817_data/vla
+# rm -rf /home/kych/datasets/fractal20220817_data/ffv1
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[34000:] --max_workers 16
+# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/ffv1 --version 0.1.0 --split train[0:] --max_workers 8 --lossless
+
diff --git a/pyproject.toml b/pyproject.toml
index 6d41820..ea399e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,16 +4,14 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "fog_x"
-version = "0.1.0.beta.4"
+version = "0.2.0"
 dependencies = [
-    "pandas", 
     "numpy",
-    "polars",
     "pillow",
-    "pyarrow",
-    "opencv-python",
-    "sqlalchemy==1.4.51",
     "smart_open",
+    "av",
+    "requests",
+    "h5py",
 ]
 description = "An Efficient and Scalable Data Collection and Management Framework For Robotics Learning"
 readme = {file = "README.md", content-type = "text/markdown"}
diff --git a/vla_to_hdf5.sh b/vla_to_hdf5.sh
new file mode 100755
index 0000000..a83e86e
--- /dev/null
+++ b/vla_to_hdf5.sh
@@ -0,0 +1,6 @@
+# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14
+
+# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14
+python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/hdf5 --max_workers 1
+
+python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name bridge --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14
\ No newline at end of file