From fe65a1ec7691b975d08a49b0fcd9a92f8f0b0daf Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Tue, 24 Sep 2024 17:16:31 +0000
Subject: [PATCH 01/25] Add ConformalIntervals class

---
 nbs/utils.ipynb           | 62 ++++++++++++++++++++++++++++++++++++++-
 neuralforecast/_modidx.py |  8 ++++-
 neuralforecast/utils.py   | 40 +++++++++++++++++++++++--
 3 files changed, 106 insertions(+), 4 deletions(-)

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index 0f935905f..2568980c8 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -38,7 +38,7 @@
     "#| export\n",
     "import random\n",
     "from itertools import chain\n",
-    "from typing import List\n",
+    "from typing import List, Union\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd"
@@ -547,6 +547,66 @@
     "        raise ValueError(f'The following values are missing from the index: {missing}')\n",
     "    return idxs"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5. Conformal Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "class ConformalIntervals:\n",
+    "    \"\"\"Class for storing conformal intervals metadata information.\"\"\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        n_windows: int = 2,\n",
+    "        method: str = \"conformal_distribution\",\n",
+    "        level: List[Union[int, float]] | None = None,\n",
+    "    ):\n",
+    "        \"\"\" \n",
+    "        n_windows : int\n",
+    "            Number of windows to evaluate.\n",
+    "        method : str, default is conformal_distribution\n",
+    "            One of the supported methods for the computation of conformal prediction:\n",
+    "            conformal_error or conformal_distribution\n",
+    "        level : list of ints or floats\n",
+    "            Confidence levels between 0 and 100 for conformal prediction intervals.\n",
+    "        \"\"\"\n",
+    "        if level is None:\n",
+    "            raise ValueError(\n",
+    "                \"level is not specified for ConformalPrediction class\"\n",
+    "            )\n",
+    "\n",
+    "        if n_windows < 2:\n",
+    "            raise ValueError(\n",
+    "                \"You need at least two windows to compute conformal intervals\"\n",
+    "            )\n",
+    "        allowed_methods = [\"conformal_error\", \"conformal_distribution\"]\n",
+    "        if method not in allowed_methods:\n",
+    "            raise ValueError(f\"method must be one of {allowed_methods}\")\n",
+    "        self.n_windows = n_windows\n",
+    "        self.method = method\n",
+    "        self.level = level\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}', level={self.level})\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/neuralforecast/_modidx.py b/neuralforecast/_modidx.py
index 6fcba7d61..064bb7dd1 100644
--- a/neuralforecast/_modidx.py
+++ b/neuralforecast/_modidx.py
@@ -1442,7 +1442,13 @@
                                                                                       'neuralforecast/tsdataset.py'),
                                           'neuralforecast.tsdataset._FilesDataset.__init__': ( 'tsdataset.html#_filesdataset.__init__',
                                                                                                'neuralforecast/tsdataset.py')},
-            'neuralforecast.utils': { 'neuralforecast.utils.DayOfMonth': ('utils.html#dayofmonth', 'neuralforecast/utils.py'),
+            'neuralforecast.utils': { 'neuralforecast.utils.ConformalIntervals': ( 'utils.html#conformalintervals',
+                                                                                   'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.ConformalIntervals.__init__': ( 'utils.html#conformalintervals.__init__',
+                                                                                            'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.ConformalIntervals.__repr__': ( 'utils.html#conformalintervals.__repr__',
+                                                                                            'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.DayOfMonth': ('utils.html#dayofmonth', 'neuralforecast/utils.py'),
                                       'neuralforecast.utils.DayOfMonth.__call__': ( 'utils.html#dayofmonth.__call__',
                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.DayOfWeek': ('utils.html#dayofweek', 'neuralforecast/utils.py'),
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index 93bb2fda1..37f391772 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -4,12 +4,12 @@
 __all__ = ['AirPassengers', 'AirPassengersDF', 'unique_id', 'ds', 'y', 'AirPassengersPanel', 'snaive', 'airline1_dummy',
            'airline2_dummy', 'AirPassengersStatic', 'generate_series', 'TimeFeature', 'SecondOfMinute', 'MinuteOfHour',
            'HourOfDay', 'DayOfWeek', 'DayOfMonth', 'DayOfYear', 'MonthOfYear', 'WeekOfYear',
-           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing']
+           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing', 'ConformalIntervals']
 
 # %% ../nbs/utils.ipynb 3
 import random
 from itertools import chain
-from typing import List
+from typing import List, Union
 
 import numpy as np
 import pandas as pd
@@ -446,3 +446,39 @@ def get_indexer_raise_missing(idx: pd.Index, vals: List[str]) -> List[int]:
     if missing:
         raise ValueError(f"The following values are missing from the index: {missing}")
     return idxs
+
+# %% ../nbs/utils.ipynb 30
+class ConformalIntervals:
+    """Class for storing conformal intervals metadata information."""
+
+    def __init__(
+        self,
+        n_windows: int = 2,
+        method: str = "conformal_distribution",
+        level: List[Union[int, float]] | None = None,
+    ):
+        """
+        n_windows : int
+            Number of windows to evaluate.
+        method : str, default is conformal_distribution
+            One of the supported methods for the computation of conformal prediction:
+            conformal_error or conformal_distribution
+        level : list of ints or floats
+            Confidence levels between 0 and 100 for conformal prediction intervals.
+        """
+        if level is None:
+            raise ValueError("level is not specified for ConformalPrediction class")
+
+        if n_windows < 2:
+            raise ValueError(
+                "You need at least two windows to compute conformal intervals"
+            )
+        allowed_methods = ["conformal_error", "conformal_distribution"]
+        if method not in allowed_methods:
+            raise ValueError(f"method must be one of {allowed_methods}")
+        self.n_windows = n_windows
+        self.method = method
+        self.level = level
+
+    def __repr__(self):
+        return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}', level={self.level})"

From df9528f0740d9ebe51c85143991ea2b876c52a3e Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 30 Sep 2024 14:37:09 +0000
Subject: [PATCH 02/25] remove level parameter from conformalInterval class

---
 nbs/utils.ipynb         | 13 ++-----------
 neuralforecast/utils.py | 11 ++---------
 2 files changed, 4 insertions(+), 20 deletions(-)

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index 2568980c8..14e98bb61 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -38,7 +38,7 @@
     "#| export\n",
     "import random\n",
     "from itertools import chain\n",
-    "from typing import List, Union\n",
+    "from typing import List\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd"
@@ -570,7 +570,6 @@
     "        self,\n",
     "        n_windows: int = 2,\n",
     "        method: str = \"conformal_distribution\",\n",
-    "        level: List[Union[int, float]] | None = None,\n",
     "    ):\n",
     "        \"\"\" \n",
     "        n_windows : int\n",
@@ -578,14 +577,7 @@
     "        method : str, default is conformal_distribution\n",
     "            One of the supported methods for the computation of conformal prediction:\n",
     "            conformal_error or conformal_distribution\n",
-    "        level : list of ints or floats\n",
-    "            Confidence levels between 0 and 100 for conformal prediction intervals.\n",
     "        \"\"\"\n",
-    "        if level is None:\n",
-    "            raise ValueError(\n",
-    "                \"level is not specified for ConformalPrediction class\"\n",
-    "            )\n",
-    "\n",
     "        if n_windows < 2:\n",
     "            raise ValueError(\n",
     "                \"You need at least two windows to compute conformal intervals\"\n",
@@ -595,10 +587,9 @@
     "            raise ValueError(f\"method must be one of {allowed_methods}\")\n",
     "        self.n_windows = n_windows\n",
     "        self.method = method\n",
-    "        self.level = level\n",
     "\n",
     "    def __repr__(self):\n",
-    "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}', level={self.level})\""
+    "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')\""
    ]
   },
   {
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index 37f391772..e5429d803 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -9,7 +9,7 @@
 # %% ../nbs/utils.ipynb 3
 import random
 from itertools import chain
-from typing import List, Union
+from typing import List
 
 import numpy as np
 import pandas as pd
@@ -455,7 +455,6 @@ def __init__(
         self,
         n_windows: int = 2,
         method: str = "conformal_distribution",
-        level: List[Union[int, float]] | None = None,
     ):
         """
         n_windows : int
@@ -463,12 +462,7 @@ def __init__(
         method : str, default is conformal_distribution
             One of the supported methods for the computation of conformal prediction:
             conformal_error or conformal_distribution
-        level : list of ints or floats
-            Confidence levels between 0 and 100 for conformal prediction intervals.
         """
-        if level is None:
-            raise ValueError("level is not specified for ConformalPrediction class")
-
         if n_windows < 2:
             raise ValueError(
                 "You need at least two windows to compute conformal intervals"
@@ -478,7 +472,6 @@ def __init__(
             raise ValueError(f"method must be one of {allowed_methods}")
         self.n_windows = n_windows
         self.method = method
-        self.level = level
 
     def __repr__(self):
-        return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}', level={self.level})"
+        return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')"

From 6a4da1211b0ed2f0b37642184c513c52e6d77087 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 01:12:33 +0000
Subject: [PATCH 03/25] conformal fit and predict logic stored in utils file

---
 nbs/utils.ipynb           | 117 +++++++++++++++++++++++++++++++++++++-
 neuralforecast/_modidx.py |   6 ++
 neuralforecast/utils.py   | 104 ++++++++++++++++++++++++++++++---
 3 files changed, 216 insertions(+), 11 deletions(-)

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index 14e98bb61..0352ed960 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -38,10 +38,12 @@
     "#| export\n",
     "import random\n",
     "from itertools import chain\n",
-    "from typing import List\n",
+    "from typing import List, Union\n",
+    "from utilsforecast.compat import DataFrame\n",
     "\n",
     "import numpy as np\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import utilsforecast.processing as ufp"
    ]
   },
   {
@@ -148,6 +150,13 @@
     "    return temporal_df"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -597,7 +606,109 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#| export\n",
+    "\n",
+    "def add_conformal_distribution_intervals(\n",
+    "    fcst_df: DataFrame, \n",
+    "    cs_df: DataFrame,\n",
+    "    model_names: List[str],\n",
+    "    level: List[Union[int, float]],\n",
+    "    cs_n_windows: int,\n",
+    "    n_series: int,\n",
+    "    horizon: int,\n",
+    ") -> DataFrame:\n",
+    "    \"\"\"\n",
+    "    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.\n",
+    "    `level` should be already sorted. This strategy creates forecasts paths\n",
+    "    based on errors and calculate quantiles using those paths.\n",
+    "    \"\"\"\n",
+    "    fcst_df = ufp.copy_if_pandas(fcst_df, deep=False)\n",
+    "    alphas = [100 - lv for lv in level]\n",
+    "    cuts = [alpha / 200 for alpha in reversed(alphas)]\n",
+    "    cuts.extend(1 - alpha / 200 for alpha in alphas)\n",
+    "    for model in model_names:\n",
+    "        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)\n",
+    "        # restrict scores to horizon\n",
+    "        scores = scores[:,:,:horizon]\n",
+    "        mean = fcst_df[model].to_numpy().reshape(1, n_series, -1)\n",
+    "        scores = np.vstack([mean - scores, mean + scores])\n",
+    "        quantiles = np.quantile(\n",
+    "            scores,\n",
+    "            cuts,\n",
+    "            axis=0,\n",
+    "        )\n",
+    "        quantiles = quantiles.reshape(len(cuts), -1).T\n",
+    "        lo_cols = [f\"{model}-conformal-lo-{lv}\" for lv in reversed(level)]\n",
+    "        hi_cols = [f\"{model}-conformal-hi-{lv}\" for lv in level]\n",
+    "        out_cols = lo_cols + hi_cols\n",
+    "        fcst_df = ufp.assign_columns(fcst_df, out_cols, quantiles)\n",
+    "    return fcst_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "def add_conformal_error_intervals(\n",
+    "    fcst_df: DataFrame, \n",
+    "    cs_df: DataFrame, \n",
+    "    model_names: List[str],\n",
+    "    level: List[Union[int, float]],\n",
+    "    cs_n_windows: int,\n",
+    "    n_series: int,\n",
+    "    horizon: int,\n",
+    ") -> DataFrame:\n",
+    "    \"\"\"\n",
+    "    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.\n",
+    "    `level` should be already sorted. This startegy creates prediction intervals\n",
+    "    based on the absolute errors.\n",
+    "    \"\"\"\n",
+    "    fcst_df = ufp.copy_if_pandas(fcst_df, deep=False)\n",
+    "    cuts = [lv / 100 for lv in level]\n",
+    "    for model in model_names:\n",
+    "        mean = fcst_df[model].to_numpy().ravel()\n",
+    "        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)\n",
+    "        # restrict scores to horizon\n",
+    "        scores = scores[:,:,:horizon]\n",
+    "        quantiles = np.quantile(\n",
+    "            scores,\n",
+    "            cuts,\n",
+    "            axis=0,\n",
+    "        )\n",
+    "        quantiles = quantiles.reshape(len(cuts), -1)\n",
+    "        lo_cols = [f\"{model}-conformal-lo-{lv}\" for lv in reversed(level)]\n",
+    "        hi_cols = [f\"{model}-conformal-hi-{lv}\" for lv in level]\n",
+    "        quantiles = np.vstack([mean - quantiles[::-1], mean + quantiles]).T\n",
+    "        columns = lo_cols + hi_cols\n",
+    "        fcst_df = ufp.assign_columns(fcst_df, columns, quantiles)\n",
+    "    return fcst_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "def get_conformal_method(method: str):\n",
+    "    available_methods = {\n",
+    "        \"conformal_distribution\": add_conformal_distribution_intervals,\n",
+    "        \"conformal_error\": add_conformal_error_intervals,\n",
+    "    }\n",
+    "    if method not in available_methods.keys():\n",
+    "        raise ValueError(\n",
+    "            f\"prediction intervals method {method} not supported \"\n",
+    "            f'please choose one of {\", \".join(available_methods.keys())}'\n",
+    "        )\n",
+    "    return available_methods[method]"
+   ]
   }
  ],
  "metadata": {
diff --git a/neuralforecast/_modidx.py b/neuralforecast/_modidx.py
index 064bb7dd1..493122490 100644
--- a/neuralforecast/_modidx.py
+++ b/neuralforecast/_modidx.py
@@ -1479,9 +1479,15 @@
                                       'neuralforecast.utils.WeekOfYear': ('utils.html#weekofyear', 'neuralforecast/utils.py'),
                                       'neuralforecast.utils.WeekOfYear.__call__': ( 'utils.html#weekofyear.__call__',
                                                                                     'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.add_conformal_distribution_intervals': ( 'utils.html#add_conformal_distribution_intervals',
+                                                                                                     'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.add_conformal_error_intervals': ( 'utils.html#add_conformal_error_intervals',
+                                                                                              'neuralforecast/utils.py'),
                                       'neuralforecast.utils.augment_calendar_df': ( 'utils.html#augment_calendar_df',
                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.generate_series': ('utils.html#generate_series', 'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.get_conformal_method': ( 'utils.html#get_conformal_method',
+                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.get_indexer_raise_missing': ( 'utils.html#get_indexer_raise_missing',
                                                                                           'neuralforecast/utils.py'),
                                       'neuralforecast.utils.time_features_from_frequency_str': ( 'utils.html#time_features_from_frequency_str',
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index e5429d803..bce62c60f 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -4,15 +4,18 @@
 __all__ = ['AirPassengers', 'AirPassengersDF', 'unique_id', 'ds', 'y', 'AirPassengersPanel', 'snaive', 'airline1_dummy',
            'airline2_dummy', 'AirPassengersStatic', 'generate_series', 'TimeFeature', 'SecondOfMinute', 'MinuteOfHour',
            'HourOfDay', 'DayOfWeek', 'DayOfMonth', 'DayOfYear', 'MonthOfYear', 'WeekOfYear',
-           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing', 'ConformalIntervals']
+           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing', 'ConformalIntervals',
+           'add_conformal_distribution_intervals', 'add_conformal_error_intervals', 'get_conformal_method']
 
 # %% ../nbs/utils.ipynb 3
 import random
 from itertools import chain
-from typing import List
+from typing import List, Union
+from utilsforecast.compat import DataFrame
 
 import numpy as np
 import pandas as pd
+import utilsforecast.processing as ufp
 
 # %% ../nbs/utils.ipynb 6
 def generate_series(
@@ -95,7 +98,7 @@ def generate_series(
 
     return temporal_df
 
-# %% ../nbs/utils.ipynb 11
+# %% ../nbs/utils.ipynb 12
 AirPassengers = np.array(
     [
         112.0,
@@ -246,7 +249,7 @@ def generate_series(
     dtype=np.float32,
 )
 
-# %% ../nbs/utils.ipynb 12
+# %% ../nbs/utils.ipynb 13
 AirPassengersDF = pd.DataFrame(
     {
         "unique_id": np.ones(len(AirPassengers)),
@@ -257,7 +260,7 @@ def generate_series(
     }
 )
 
-# %% ../nbs/utils.ipynb 19
+# %% ../nbs/utils.ipynb 20
 # Declare Panel Data
 unique_id = np.concatenate(
     [["Airline1"] * len(AirPassengers), ["Airline2"] * len(AirPassengers)]
@@ -292,7 +295,7 @@ def generate_series(
 
 AirPassengersPanel.groupby("unique_id").tail(4)
 
-# %% ../nbs/utils.ipynb 25
+# %% ../nbs/utils.ipynb 26
 class TimeFeature:
     def __init__(self):
         pass
@@ -439,7 +442,7 @@ def augment_calendar_df(df, freq="H"):
 
     return pd.concat([df, ds_data], axis=1), freq_map[freq]
 
-# %% ../nbs/utils.ipynb 28
+# %% ../nbs/utils.ipynb 29
 def get_indexer_raise_missing(idx: pd.Index, vals: List[str]) -> List[int]:
     idxs = idx.get_indexer(vals)
     missing = [v for i, v in zip(idxs, vals) if i == -1]
@@ -447,7 +450,7 @@ def get_indexer_raise_missing(idx: pd.Index, vals: List[str]) -> List[int]:
         raise ValueError(f"The following values are missing from the index: {missing}")
     return idxs
 
-# %% ../nbs/utils.ipynb 30
+# %% ../nbs/utils.ipynb 31
 class ConformalIntervals:
     """Class for storing conformal intervals metadata information."""
 
@@ -475,3 +478,88 @@ def __init__(
 
     def __repr__(self):
         return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')"
+
+# %% ../nbs/utils.ipynb 32
+def add_conformal_distribution_intervals(
+    fcst_df: DataFrame,
+    cs_df: DataFrame,
+    model_names: List[str],
+    level: List[Union[int, float]],
+    cs_n_windows: int,
+    n_series: int,
+    horizon: int,
+) -> DataFrame:
+    """
+    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.
+    `level` should be already sorted. This strategy creates forecasts paths
+    based on errors and calculate quantiles using those paths.
+    """
+    fcst_df = ufp.copy_if_pandas(fcst_df, deep=False)
+    alphas = [100 - lv for lv in level]
+    cuts = [alpha / 200 for alpha in reversed(alphas)]
+    cuts.extend(1 - alpha / 200 for alpha in alphas)
+    for model in model_names:
+        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)
+        # restrict scores to horizon
+        scores = scores[:, :, :horizon]
+        mean = fcst_df[model].to_numpy().reshape(1, n_series, -1)
+        scores = np.vstack([mean - scores, mean + scores])
+        quantiles = np.quantile(
+            scores,
+            cuts,
+            axis=0,
+        )
+        quantiles = quantiles.reshape(len(cuts), -1).T
+        lo_cols = [f"{model}-conformal-lo-{lv}" for lv in reversed(level)]
+        hi_cols = [f"{model}-conformal-hi-{lv}" for lv in level]
+        out_cols = lo_cols + hi_cols
+        fcst_df = ufp.assign_columns(fcst_df, out_cols, quantiles)
+    return fcst_df
+
+# %% ../nbs/utils.ipynb 33
+def add_conformal_error_intervals(
+    fcst_df: DataFrame,
+    cs_df: DataFrame,
+    model_names: List[str],
+    level: List[Union[int, float]],
+    cs_n_windows: int,
+    n_series: int,
+    horizon: int,
+) -> DataFrame:
+    """
+    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.
+    `level` should be already sorted. This startegy creates prediction intervals
+    based on the absolute errors.
+    """
+    fcst_df = ufp.copy_if_pandas(fcst_df, deep=False)
+    cuts = [lv / 100 for lv in level]
+    for model in model_names:
+        mean = fcst_df[model].to_numpy().ravel()
+        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)
+        # restrict scores to horizon
+        scores = scores[:, :, :horizon]
+        quantiles = np.quantile(
+            scores,
+            cuts,
+            axis=0,
+        )
+        quantiles = quantiles.reshape(len(cuts), -1)
+        lo_cols = [f"{model}-conformal-lo-{lv}" for lv in reversed(level)]
+        hi_cols = [f"{model}-conformal-hi-{lv}" for lv in level]
+        quantiles = np.vstack([mean - quantiles[::-1], mean + quantiles]).T
+        columns = lo_cols + hi_cols
+        fcst_df = ufp.assign_columns(fcst_df, columns, quantiles)
+    return fcst_df
+
+# %% ../nbs/utils.ipynb 34
+def get_conformal_method(method: str):
+    available_methods = {
+        "conformal_distribution": add_conformal_distribution_intervals,
+        "conformal_error": add_conformal_error_intervals,
+    }
+    if method not in available_methods.keys():
+        raise ValueError(
+            f"prediction intervals method {method} not supported "
+            f'please choose one of {", ".join(available_methods.keys())}'
+        )
+    return available_methods[method]

From e2b0c4a375a67e0611bded54fa6156b188af9201 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 01:13:42 +0000
Subject: [PATCH 04/25] Specify losses that do not support conformal prediction

---
 nbs/losses.pytorch.ipynb         | 39 ++++++++++++++++++++++++++++++++
 neuralforecast/losses/pytorch.py | 19 +++++++++++++---
 2 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/nbs/losses.pytorch.ipynb b/nbs/losses.pytorch.ipynb
index efcff01a1..aea6d3e69 100644
--- a/nbs/losses.pytorch.ipynb
+++ b/nbs/losses.pytorch.ipynb
@@ -4361,6 +4361,45 @@
     "loss = mae(y=y, y_hat=y_hat, mask=mask)\n",
     "assert loss==(1/3), 'Should be 1/3'"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99ada497",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Scaled Continuous Ranked Probability Score (sCRPS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c66abf27",
+   "metadata": {},
+   "source": [
+    "## Unsupported losses for conformal prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c960d4db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "UNSUPPORTED_LOSSES_CONFORMAL = (\n",
+    "    MQLoss,\n",
+    "    DistributionLoss,\n",
+    "    PMM,\n",
+    "    GMM,\n",
+    "    NBMM,\n",
+    "    HuberMQLoss, \n",
+    "    QuantileLoss,\n",
+    "    IQLoss,\n",
+    "    sCRPS,\n",
+    ")"
+   ]
   }
  ],
  "metadata": {
diff --git a/neuralforecast/losses/pytorch.py b/neuralforecast/losses/pytorch.py
index a65b1c532..fc0badfac 100644
--- a/neuralforecast/losses/pytorch.py
+++ b/neuralforecast/losses/pytorch.py
@@ -1,9 +1,9 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/losses.pytorch.ipynb.
 
 # %% auto 0
-__all__ = ['BasePointLoss', 'MAE', 'MSE', 'RMSE', 'MAPE', 'SMAPE', 'MASE', 'relMSE', 'QuantileLoss', 'MQLoss', 'QuantileLayer',
-           'IQLoss', 'DistributionLoss', 'PMM', 'GMM', 'NBMM', 'HuberLoss', 'TukeyLoss', 'HuberQLoss', 'HuberMQLoss',
-           'Accuracy', 'sCRPS']
+__all__ = ['UNSUPPORTED_LOSSES_CONFORMAL', 'BasePointLoss', 'MAE', 'MSE', 'RMSE', 'MAPE', 'SMAPE', 'MASE', 'relMSE',
+           'QuantileLoss', 'MQLoss', 'QuantileLayer', 'IQLoss', 'DistributionLoss', 'PMM', 'GMM', 'NBMM', 'HuberLoss',
+           'TukeyLoss', 'HuberQLoss', 'HuberMQLoss', 'Accuracy', 'sCRPS']
 
 # %% ../../nbs/losses.pytorch.ipynb 4
 from typing import Optional, Union, Tuple
@@ -3089,3 +3089,16 @@ def __call__(
         unmean = torch.sum(mask)
         scrps = 2 * mql * unmean / (norm + 1e-5)
         return scrps
+
+# %% ../../nbs/losses.pytorch.ipynb 128
+UNSUPPORTED_LOSSES_CONFORMAL = (
+    MQLoss,
+    DistributionLoss,
+    PMM,
+    GMM,
+    NBMM,
+    HuberMQLoss,
+    QuantileLoss,
+    IQLoss,
+    sCRPS,
+)

From 2ea83eb4dd0ff587a147a84438684bec58c27d4c Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 01:17:10 +0000
Subject: [PATCH 05/25] conformal prediction integrated to NeuralForecast class

---
 nbs/core.ipynb            | 255 +++++++++++++++++++++++++++++++++++++-
 neuralforecast/_modidx.py |   2 +
 neuralforecast/core.py    | 104 +++++++++++++++-
 3 files changed, 357 insertions(+), 4 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index c59bbe7a3..578d1fb5c 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -95,7 +95,9 @@
     "    BiTCN, TiDE, DeepNPTS, SOFTS,\n",
     "    TimeMixer, KAN, RMoK\n",
     ")\n",
-    "from neuralforecast.common._base_auto import BaseAuto, MockTrial"
+    "from neuralforecast.common._base_auto import BaseAuto, MockTrial\n",
+    "from neuralforecast.utils import ConformalIntervals, get_conformal_method\n",
+    "from neuralforecast.losses.pytorch import UNSUPPORTED_LOSSES_CONFORMAL"
    ]
   },
   {
@@ -506,6 +508,7 @@
     "        time_col: str = 'ds',\n",
     "        target_col: str = 'y',\n",
     "        distributed_config: Optional[DistributedConfig] = None,\n",
+    "        conformal_intervals: Optional[ConformalIntervals] = None,\n",
     "    ) -> None:\n",
     "        \"\"\"Fit the core.NeuralForecast.\n",
     "\n",
@@ -535,6 +538,8 @@
     "            Column that contains the target.\n",
     "        distributed_config : neuralforecast.DistributedConfig\n",
     "            Configuration to use for DDP training. Currently only spark is supported.\n",
+    "        conformal_intervals : ConformalIntervals, optional (default=None)\n",
+    "            Configuration to calibrate prediction intervals (Conformal Prediction).            \n",
     "\n",
     "        Returns\n",
     "        -------\n",
@@ -550,6 +555,8 @@
     "            and val_size == 0\n",
     "        ):\n",
     "                raise Exception('Set val_size>0 if early stopping is enabled.')\n",
+    "        \n",
+    "        self._cs_df: Optional[DataFrame] = None\n",
     "\n",
     "        # Process and save new dataset (in self)\n",
     "        if isinstance(df, (pd.DataFrame, pl_DataFrame)):\n",
@@ -603,6 +610,17 @@
     "            if self.dataset.min_size < val_size:\n",
     "                warnings.warn('Validation set size is larger than the shorter time-series.')\n",
     "\n",
+    "        if conformal_intervals is not None:\n",
+    "            # conformal prediction\n",
+    "            self.conformal_intervals = conformal_intervals\n",
+    "            self._cs_df = self._conformity_scores(\n",
+    "                df=df,\n",
+    "                id_col=id_col,\n",
+    "                time_col=time_col,\n",
+    "                target_col=target_col,\n",
+    "                static_df=static_df,\n",
+    "            )\n",
+    "\n",
     "        # Recover initial model if use_init_models\n",
     "        if use_init_models:\n",
     "            self._reset_models()\n",
@@ -708,10 +726,14 @@
     "\n",
     "        return futr_exog | set(hist_exog)\n",
     "    \n",
-    "    def _get_model_names(self) -> List[str]:\n",
+    "    def _get_model_names(self, use_conformal=False) -> List[str]:\n",
     "        names: List[str] = []\n",
     "        count_names = {'model': 0}\n",
     "        for model in self.models:\n",
+    "            # skip model for consideration of conformal prediction\n",
+    "            if use_conformal and isinstance(model.loss, UNSUPPORTED_LOSSES_CONFORMAL):\n",
+    "                continue\n",
+    "\n",
     "            model_name = repr(model)\n",
     "            count_names[model_name] = count_names.get(model_name, -1) + 1\n",
     "            if count_names[model_name] > 0:\n",
@@ -834,6 +856,7 @@
     "        sort_df: bool = True,\n",
     "        verbose: bool = False,\n",
     "        engine = None,\n",
+    "        conformal_level: Optional[List[Union[int, float]]] = None,\n",
     "        **data_kwargs\n",
     "    ):\n",
     "        \"\"\"Predict with core.NeuralForecast.\n",
@@ -855,6 +878,8 @@
     "            Print processing steps.\n",
     "        engine : spark session\n",
     "            Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.\n",
+    "        conformal_level : list of ints or floats, optional (default=None)\n",
+    "            Confidence levels between 0 and 100 for conformal intervals.\n",
     "        data_kwargs : kwargs\n",
     "            Extra arguments to be passed to the dataset within each model.\n",
     "\n",
@@ -989,6 +1014,29 @@
     "        if isinstance(fcsts_df, pd.DataFrame) and _id_as_idx():\n",
     "            _warn_id_as_idx()\n",
     "            fcsts_df = fcsts_df.set_index(self.id_col)\n",
+    "\n",
+    "        # perform conformal predictions\n",
+    "        if conformal_level is not None:\n",
+    "            if self._cs_df is None:\n",
+    "                warn_msg = (\n",
+    "                    'Please rerun the `fit` method passing a valid confrmal_interval settings to compute conformity scores'\n",
+    "                )\n",
+    "                warnings.warn(warn_msg, UserWarning)\n",
+    "            else:\n",
+    "                level_ = sorted(conformal_level)\n",
+    "                model_names = self._get_model_names(use_conformal=True)\n",
+    "                conformal_method = get_conformal_method(self.conformal_intervals.method)\n",
+    "\n",
+    "                fcsts_df = conformal_method(\n",
+    "                    fcsts_df,\n",
+    "                    self._cs_df,\n",
+    "                    model_names=list(model_names),\n",
+    "                    level=level_,\n",
+    "                    cs_n_windows=self.conformal_intervals.n_windows,\n",
+    "                    n_series=len(uids),\n",
+    "                    horizon=self.h,\n",
+    "                )\n",
+    "\n",
     "        return fcsts_df\n",
     "\n",
     "    def _reset_models(self):\n",
@@ -1474,6 +1522,8 @@
     "            \"id_col\": self.id_col,\n",
     "            \"time_col\": self.time_col,\n",
     "            \"target_col\": self.target_col,\n",
+    "            # conformity scores for conformal prediction\n",
+    "            \"_cs_df\": self._cs_df,\n",
     "        }\n",
     "        if save_dataset:\n",
     "            config_dict.update(\n",
@@ -1561,6 +1611,10 @@
     "\n",
     "        for attr in ['id_col', 'time_col', 'target_col']:\n",
     "            setattr(neuralforecast, attr, config_dict[attr])\n",
+    "        # only restore attribute if available\n",
+    "        for attr in ['_cs_df']:\n",
+    "            if attr in config_dict.keys():\n",
+    "                setattr(neuralforecast, attr, config_dict[attr])\n",
     "\n",
     "        # Dataset\n",
     "        if dataset is not None:\n",
@@ -1579,7 +1633,57 @@
     "\n",
     "        neuralforecast.scalers_ = config_dict['scalers_']\n",
     "\n",
-    "        return neuralforecast"
+    "        return neuralforecast\n",
+    "    \n",
+    "    def _conformity_scores(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        id_col: str, \n",
+    "        time_col: str,\n",
+    "        target_col: str,\n",
+    "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
+    "    ) -> DataFrame:\n",
+    "        \"\"\"Compute conformity scores.\n",
+    "        \n",
+    "        We need at least two cross validation errors to compute\n",
+    "        quantiles for prediction intervals (`n_windows=2`, specified by self.conformal_intervals).\n",
+    "        \n",
+    "        The exception is raised by the ConformalIntervals data class.\n",
+    "\n",
+    "        df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,\n",
+    "        id_col: str = 'unique_id',\n",
+    "        time_col: str = 'ds',\n",
+    "        target_col: str = 'y',\n",
+    "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
+    "        \"\"\"\n",
+    "        min_size = ufp.counts_by_id(df, id_col)['counts'].min()\n",
+    "        min_samples = self.h * self.conformal_intervals.n_windows + 1\n",
+    "        if min_size < min_samples:\n",
+    "            raise ValueError(\n",
+    "                \"Minimum required samples in each serie for the prediction intervals \"\n",
+    "                f\"settings are: {min_samples}, shortest serie has: {min_size}. \"\n",
+    "                \"Please reduce the number of windows, horizon or remove those series.\"\n",
+    "            )\n",
+    "        \n",
+    "        cv_results = self.cross_validation(\n",
+    "            df=df,\n",
+    "            static_df=static_df,\n",
+    "            n_windows=self.conformal_intervals.n_windows,\n",
+    "            id_col=id_col,\n",
+    "            time_col=time_col,\n",
+    "            target_col=target_col,\n",
+    "        )\n",
+    "        \n",
+    "        kept = [time_col, id_col, 'cutoff']\n",
+    "        # conformity score for each model\n",
+    "        for model in self._get_model_names(use_conformal=True):\n",
+    "            kept.append(model)\n",
+    "\n",
+    "            # compute absolute error for each model\n",
+    "            abs_err = abs(cv_results[model] - cv_results[target_col])\n",
+    "            cv_results = ufp.assign_columns(cv_results, model, abs_err)\n",
+    "        dropped = list(set(cv_results.columns) - set(kept))\n",
+    "        return ufp.drop_columns(cv_results, dropped)    "
    ]
   },
   {
@@ -1806,6 +1910,14 @@
     "test_eq(init_fcst, after_fcst)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d94486f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2506,6 +2618,7 @@
     "    ],\n",
     "    freq='M'\n",
     ")\n",
+    "# JQTODO modify this for test\n",
     "fcst.fit(AirPassengersPanel_train)\n",
     "forecasts1 = fcst.predict(futr_df=AirPassengersPanel_test)\n",
     "save_paths = ['./examples/debug_run/']\n",
@@ -3204,6 +3317,142 @@
     "        nf.fit(AirPassengersPanel_train)\n",
     "        assert any(\"ignoring lr_scheduler_kwargs as the lr_scheduler is not specified\" in str(w.message) for w in issued_warnings)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0441e9d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# test conformal prediction, method=conformal_distribution\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals()\n",
+    "\n",
+    "models = []\n",
+    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
+    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "        params.update({\"n_series\": 2})\n",
+    "    models.append(nf_model(**params))\n",
+    "\n",
+    "\n",
+    "nf = NeuralForecast(models=models, freq='M')\n",
+    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee2f2f84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
+    "# only select those from NHITS to show\n",
+    "preds = preds.reset_index(drop=True)\n",
+    "dropped = [x for x in preds.columns if 'StemGNN' in x or 'RNN' in x]\n",
+    "preds = preds.drop(columns=dropped)\n",
+    "\n",
+    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
+    "\n",
+    "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
+    "\n",
+    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
+    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
+    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
+    "ax.legend(prop={'size': 15})\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9eb89f3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "#| polars\n",
+    "# test conformal prediction works for polar dataframe\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals()\n",
+    "\n",
+    "models = []\n",
+    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
+    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "        params.update({\"n_series\": 2})\n",
+    "    models.append(nf_model(**params))\n",
+    "\n",
+    "\n",
+    "nf = NeuralForecast(models=models, freq='1mo')\n",
+    "nf.fit(AirPassengers_pl, conformal_intervals=conformal_intervals, time_col='time', id_col='uid', target_col='target')\n",
+    "preds = nf.predict(conformal_level=[10, 50, 90])\n",
+    "\n",
+    "print(preds)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0db88cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# test conformal prediction, method=conformal_error\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals(method=\"conformal_error\")\n",
+    "\n",
+    "models = []\n",
+    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
+    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "        params.update({\"n_series\": 2})\n",
+    "    models.append(nf_model(**params))\n",
+    "\n",
+    "\n",
+    "nf = NeuralForecast(models=models, freq='M')\n",
+    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d25b2cd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# test conformal prediction are not applied for models with quantiled-related loss\n",
+    "# only those supported losses shall export columns with '*-conformal-*'\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals()\n",
+    "\n",
+    "models = []\n",
+    "for nf_model in [NHITS, RNN]:\n",
+    "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
+    "    if nf_model.__name__ == \"NHITS\":\n",
+    "        params.update({\"loss\": MQLoss(level=[80])})\n",
+    "    models.append(nf_model(**params))\n",
+    "\n",
+    "\n",
+    "nf = NeuralForecast(models=models, freq='M')\n",
+    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])\n",
+    "\n",
+    "pred_cols = [\n",
+    "    'NHITS-median', 'NHITS-lo-80', 'NHITS-hi-80', 'RNN',\n",
+    "    'RNN-conformal-lo-90', 'RNN-conformal-lo-50', 'RNN-conformal-lo-10',\n",
+    "    'RNN-conformal-hi-10', 'RNN-conformal-hi-50', 'RNN-conformal-hi-90'\n",
+    "]\n",
+    "assert all([col in preds.columns for col in pred_cols])\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/neuralforecast/_modidx.py b/neuralforecast/_modidx.py
index 493122490..41ae2929f 100644
--- a/neuralforecast/_modidx.py
+++ b/neuralforecast/_modidx.py
@@ -162,6 +162,8 @@
                                                                                       'neuralforecast/core.py'),
                                      'neuralforecast.core.NeuralForecast._check_nan': ( 'core.html#neuralforecast._check_nan',
                                                                                         'neuralforecast/core.py'),
+                                     'neuralforecast.core.NeuralForecast._conformity_scores': ( 'core.html#neuralforecast._conformity_scores',
+                                                                                                'neuralforecast/core.py'),
                                      'neuralforecast.core.NeuralForecast._get_model_names': ( 'core.html#neuralforecast._get_model_names',
                                                                                               'neuralforecast/core.py'),
                                      'neuralforecast.core.NeuralForecast._get_needed_exog': ( 'core.html#neuralforecast._get_needed_exog',
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index b21815b69..073109aa3 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -69,6 +69,8 @@
     RMoK,
 )
 from .common._base_auto import BaseAuto, MockTrial
+from .utils import ConformalIntervals, get_conformal_method
+from .losses.pytorch import UNSUPPORTED_LOSSES_CONFORMAL
 
 # %% ../nbs/core.ipynb 5
 # this disables warnings about the number of workers in the dataloaders
@@ -438,6 +440,7 @@ def fit(
         time_col: str = "ds",
         target_col: str = "y",
         distributed_config: Optional[DistributedConfig] = None,
+        conformal_intervals: Optional[ConformalIntervals] = None,
     ) -> None:
         """Fit the core.NeuralForecast.
 
@@ -467,6 +470,8 @@ def fit(
             Column that contains the target.
         distributed_config : neuralforecast.DistributedConfig
             Configuration to use for DDP training. Currently only spark is supported.
+        conformal_intervals : ConformalIntervals, optional (default=None)
+            Configuration to calibrate prediction intervals (Conformal Prediction).
 
         Returns
         -------
@@ -483,6 +488,8 @@ def fit(
         ):
             raise Exception("Set val_size>0 if early stopping is enabled.")
 
+        self._cs_df: Optional[DataFrame] = None
+
         # Process and save new dataset (in self)
         if isinstance(df, (pd.DataFrame, pl_DataFrame)):
             validate_freq(df[time_col], self.freq)
@@ -539,6 +546,17 @@ def fit(
                     "Validation set size is larger than the shorter time-series."
                 )
 
+        if conformal_intervals is not None:
+            # conformal prediction
+            self.conformal_intervals = conformal_intervals
+            self._cs_df = self._conformity_scores(
+                df=df,
+                id_col=id_col,
+                time_col=time_col,
+                target_col=target_col,
+                static_df=static_df,
+            )
+
         # Recover initial model if use_init_models
         if use_init_models:
             self._reset_models()
@@ -648,10 +666,14 @@ def _get_needed_exog(self):
 
         return futr_exog | set(hist_exog)
 
-    def _get_model_names(self) -> List[str]:
+    def _get_model_names(self, use_conformal=False) -> List[str]:
         names: List[str] = []
         count_names = {"model": 0}
         for model in self.models:
+            # skip model for consideration of conformal prediction
+            if use_conformal and isinstance(model.loss, UNSUPPORTED_LOSSES_CONFORMAL):
+                continue
+
             model_name = repr(model)
             count_names[model_name] = count_names.get(model_name, -1) + 1
             if count_names[model_name] > 0:
@@ -782,6 +804,7 @@ def predict(
         sort_df: bool = True,
         verbose: bool = False,
         engine=None,
+        conformal_level: Optional[List[Union[int, float]]] = None,
         **data_kwargs,
     ):
         """Predict with core.NeuralForecast.
@@ -803,6 +826,8 @@ def predict(
             Print processing steps.
         engine : spark session
             Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.
+        conformal_level : list of ints or floats, optional (default=None)
+            Confidence levels between 0 and 100 for conformal intervals.
         data_kwargs : kwargs
             Extra arguments to be passed to the dataset within each model.
 
@@ -943,6 +968,27 @@ def predict(
         if isinstance(fcsts_df, pd.DataFrame) and _id_as_idx():
             _warn_id_as_idx()
             fcsts_df = fcsts_df.set_index(self.id_col)
+
+        # perform conformal predictions
+        if conformal_level is not None:
+            if self._cs_df is None:
+                warn_msg = "Please rerun the `fit` method passing a valid confrmal_interval settings to compute conformity scores"
+                warnings.warn(warn_msg, UserWarning)
+            else:
+                level_ = sorted(conformal_level)
+                model_names = self._get_model_names(use_conformal=True)
+                conformal_method = get_conformal_method(self.conformal_intervals.method)
+
+                fcsts_df = conformal_method(
+                    fcsts_df,
+                    self._cs_df,
+                    model_names=list(model_names),
+                    level=level_,
+                    cs_n_windows=self.conformal_intervals.n_windows,
+                    n_series=len(uids),
+                    horizon=self.h,
+                )
+
         return fcsts_df
 
     def _reset_models(self):
@@ -1452,6 +1498,8 @@ def save(
             "id_col": self.id_col,
             "time_col": self.time_col,
             "target_col": self.target_col,
+            # conformity scores for conformal prediction
+            "_cs_df": self._cs_df,
         }
         if save_dataset:
             config_dict.update(
@@ -1548,6 +1596,10 @@ def load(path, verbose=False, **kwargs):
 
         for attr in ["id_col", "time_col", "target_col"]:
             setattr(neuralforecast, attr, config_dict[attr])
+        # only restore attribute if available
+        for attr in ["_cs_df"]:
+            if attr in config_dict.keys():
+                setattr(neuralforecast, attr, config_dict[attr])
 
         # Dataset
         if dataset is not None:
@@ -1567,3 +1619,53 @@ def load(path, verbose=False, **kwargs):
         neuralforecast.scalers_ = config_dict["scalers_"]
 
         return neuralforecast
+
+    def _conformity_scores(
+        self,
+        df: DataFrame,
+        id_col: str,
+        time_col: str,
+        target_col: str,
+        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
+    ) -> DataFrame:
+        """Compute conformity scores.
+
+        We need at least two cross validation errors to compute
+        quantiles for prediction intervals (`n_windows=2`, specified by self.conformal_intervals).
+
+        The exception is raised by the ConformalIntervals data class.
+
+        df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,
+        id_col: str = 'unique_id',
+        time_col: str = 'ds',
+        target_col: str = 'y',
+        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
+        """
+        min_size = ufp.counts_by_id(df, id_col)["counts"].min()
+        min_samples = self.h * self.conformal_intervals.n_windows + 1
+        if min_size < min_samples:
+            raise ValueError(
+                "Minimum required samples in each serie for the prediction intervals "
+                f"settings are: {min_samples}, shortest serie has: {min_size}. "
+                "Please reduce the number of windows, horizon or remove those series."
+            )
+
+        cv_results = self.cross_validation(
+            df=df,
+            static_df=static_df,
+            n_windows=self.conformal_intervals.n_windows,
+            id_col=id_col,
+            time_col=time_col,
+            target_col=target_col,
+        )
+
+        kept = [time_col, id_col, "cutoff"]
+        # conformity score for each model
+        for model in self._get_model_names(use_conformal=True):
+            kept.append(model)
+
+            # compute absolute error for each model
+            abs_err = abs(cv_results[model] - cv_results[target_col])
+            cv_results = ufp.assign_columns(cv_results, model, abs_err)
+        dropped = list(set(cv_results.columns) - set(kept))
+        return ufp.drop_columns(cv_results, dropped)

From 6aaa80b9af4d8ff901f7eceefad168b6005f92a7 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 13:24:47 +0000
Subject: [PATCH 06/25] HuberQLoss does not support conformal prediction

---
 nbs/losses.pytorch.ipynb         | 1 +
 neuralforecast/losses/pytorch.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/nbs/losses.pytorch.ipynb b/nbs/losses.pytorch.ipynb
index aea6d3e69..dae834be5 100644
--- a/nbs/losses.pytorch.ipynb
+++ b/nbs/losses.pytorch.ipynb
@@ -4394,6 +4394,7 @@
     "    PMM,\n",
     "    GMM,\n",
     "    NBMM,\n",
+    "    HuberQLoss,\n",
     "    HuberMQLoss, \n",
     "    QuantileLoss,\n",
     "    IQLoss,\n",
diff --git a/neuralforecast/losses/pytorch.py b/neuralforecast/losses/pytorch.py
index fc0badfac..908958a99 100644
--- a/neuralforecast/losses/pytorch.py
+++ b/neuralforecast/losses/pytorch.py
@@ -3097,6 +3097,7 @@ def __call__(
     PMM,
     GMM,
     NBMM,
+    HuberQLoss,
     HuberMQLoss,
     QuantileLoss,
     IQLoss,

From 20e1672cb26a8ef728094bef80c5345748ec8952 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 13:33:27 +0000
Subject: [PATCH 07/25] Remove unncessary illustration

---
 nbs/core.ipynb | 29 +----------------------------
 1 file changed, 1 insertion(+), 28 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 578d1fb5c..6a9ea330f 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -3343,31 +3343,6 @@
     "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee2f2f84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
-    "# only select those from NHITS to show\n",
-    "preds = preds.reset_index(drop=True)\n",
-    "dropped = [x for x in preds.columns if 'StemGNN' in x or 'RNN' in x]\n",
-    "preds = preds.drop(columns=dropped)\n",
-    "\n",
-    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
-    "\n",
-    "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
-    "\n",
-    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
-    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
-    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
-    "ax.legend(prop={'size': 15})\n",
-    "ax.grid()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3391,9 +3366,7 @@
     "\n",
     "nf = NeuralForecast(models=models, freq='1mo')\n",
     "nf.fit(AirPassengers_pl, conformal_intervals=conformal_intervals, time_col='time', id_col='uid', target_col='target')\n",
-    "preds = nf.predict(conformal_level=[10, 50, 90])\n",
-    "\n",
-    "print(preds)\n"
+    "preds = nf.predict(conformal_level=[10, 50, 90])"
    ]
   },
   {

From 37d4f28bba0a4162a6d13ed0f100a2db6d17131f Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 13:38:15 +0000
Subject: [PATCH 08/25] Add test for model saving & loading for conformal
 predictions

---
 nbs/core.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 6a9ea330f..8be22a549 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -2618,9 +2618,9 @@
     "    ],\n",
     "    freq='M'\n",
     ")\n",
-    "# JQTODO modify this for test\n",
-    "fcst.fit(AirPassengersPanel_train)\n",
-    "forecasts1 = fcst.predict(futr_df=AirPassengersPanel_test)\n",
+    "conformal_intervals = ConformalIntervals()\n",
+    "fcst.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
+    "forecasts1 = fcst.predict(futr_df=AirPassengersPanel_test, conformal_level=[50])\n",
     "save_paths = ['./examples/debug_run/']\n",
     "try:\n",
     "    s3fs.S3FileSystem().ls('s3://nixtla-tmp')    \n",
@@ -2634,7 +2634,7 @@
     "for path in save_paths:\n",
     "    fcst.save(path=path, model_index=None, overwrite=True, save_dataset=True)\n",
     "    fcst2 = NeuralForecast.load(path=path)\n",
-    "    forecasts2 = fcst2.predict(futr_df=AirPassengersPanel_test)\n",
+    "    forecasts2 = fcst2.predict(futr_df=AirPassengersPanel_test, conformal_level=[50])\n",
     "    pd.testing.assert_frame_equal(forecasts1, forecasts2[forecasts1.columns])"
    ]
   },

From c1140f66596b58c587f62b4e19a5bc325e5ce6b5 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 13:51:46 +0000
Subject: [PATCH 09/25] Fix model saving/loading missing conformal_intervals

---
 nbs/core.ipynb         | 7 ++++---
 neuralforecast/core.py | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 8be22a549..79b27a963 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -1522,8 +1522,9 @@
     "            \"id_col\": self.id_col,\n",
     "            \"time_col\": self.time_col,\n",
     "            \"target_col\": self.target_col,\n",
-    "            # conformity scores for conformal prediction\n",
-    "            \"_cs_df\": self._cs_df,\n",
+    "            # conformal prediction\n",
+    "            \"conformal_intervals\": self.conformal_intervals,\n",
+    "            \"_cs_df\": self._cs_df,  # conformity score\n",
     "        }\n",
     "        if save_dataset:\n",
     "            config_dict.update(\n",
@@ -1612,7 +1613,7 @@
     "        for attr in ['id_col', 'time_col', 'target_col']:\n",
     "            setattr(neuralforecast, attr, config_dict[attr])\n",
     "        # only restore attribute if available\n",
-    "        for attr in ['_cs_df']:\n",
+    "        for attr in ['conformal_intervals', '_cs_df']:\n",
     "            if attr in config_dict.keys():\n",
     "                setattr(neuralforecast, attr, config_dict[attr])\n",
     "\n",
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index 073109aa3..c3677d827 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -1498,8 +1498,9 @@ def save(
             "id_col": self.id_col,
             "time_col": self.time_col,
             "target_col": self.target_col,
-            # conformity scores for conformal prediction
-            "_cs_df": self._cs_df,
+            # conformal prediction
+            "conformal_intervals": self.conformal_intervals,
+            "_cs_df": self._cs_df,  # conformity score
         }
         if save_dataset:
             config_dict.update(
@@ -1597,7 +1598,7 @@ def load(path, verbose=False, **kwargs):
         for attr in ["id_col", "time_col", "target_col"]:
             setattr(neuralforecast, attr, config_dict[attr])
         # only restore attribute if available
-        for attr in ["_cs_df"]:
+        for attr in ["conformal_intervals", "_cs_df"]:
             if attr in config_dict.keys():
                 setattr(neuralforecast, attr, config_dict[attr])
 

From 952c33225141fd2f94bdcf881306fabc804b512e Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 14:41:42 +0000
Subject: [PATCH 10/25] Add tutorial on conformal prediction

---
 .../tutorials/20_conformal_prediction.ipynb   | 230 ++++++++++++++++++
 1 file changed, 230 insertions(+)
 create mode 100644 nbs/docs/tutorials/20_conformal_prediction.ipynb

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
new file mode 100644
index 000000000..0e7589923
--- /dev/null
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conformal Prediction\n",
+    "> Tutorial on how to train neuralforecast models and obtain prediction intervals using the conformal prediction methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conformal prediction intervals use cross-validation on a point forecaster model to generate the intervals. This means that no prior probabilities are needed, and the output is well-calibrated. No additional training is needed, and the model is treated as a black box. The approach is compatible with any model.\n",
+    "\n",
+    "In this notebook, we demonstrate how to obtain intervals using the conformal prediction methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from neuralforecast import NeuralForecast\n",
+    "from neuralforecast.models import NHITS\n",
+    "from utilsforecast.losses import mae, rmse, smape\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)\n",
+    "os.environ['NIXTLA_ID_AS_COL'] = '1'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "We simply use AirPassengers dataset for the demostration of conformal predictions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AirPassengersPanel_train = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
+    "AirPassengersPanel_test = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
+    "AirPassengersPanel_test['y'] = np.nan\n",
+    "AirPassengersPanel_test['y_[lag12]'] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also create this directory structure with a spark dataframe using the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "spark.conf.set(\"spark.sql.parquet.outputTimestampType\", \"TIMESTAMP_MICROS\")\n",
+    "(\n",
+    "  spark_df\n",
+    "  .repartition(id_col)\n",
+    "  .sortWithinPartitions(id_col, time_col)\n",
+    "  .write\n",
+    "  .partitionBy(id_col)\n",
+    "  .parquet(out_dir)\n",
+    ")\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The DataLoader class still expects the static data to be passed in as a single DataFrame with one row per timeseries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "static = AirPassengersStatic.rename(columns={'unique_id': 'id_col'})\n",
+    "static"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model training\n",
+    "\n",
+    "We now train a NHITS model on the above dataset. \n",
+    "It is worth noting that NeuralForecast currently does not support scaling when using this DataLoader. If you want to scale the timeseries this should be done before passing it in to the `fit` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "horizon = 12\n",
+    "stacks = 3\n",
+    "models = [NHITS(input_size=5 * horizon,\n",
+    "                h=horizon,\n",
+    "                futr_exog_list=['trend', 'y_[lag12]'],\n",
+    "                stat_exog_list=['airline1', 'airline2'],\n",
+    "                max_steps=100,\n",
+    "                stack_types = stacks*['identity'],\n",
+    "                n_blocks = stacks*[1],\n",
+    "                mlp_units = [[256,256] for _ in range(stacks)],\n",
+    "                n_pool_kernel_size = stacks*[1])]\n",
+    "nf = NeuralForecast(models=models, freq='ME')\n",
+    "nf.fit(df=files_list, static_df=static, id_col='id_col')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Forecasting\n",
+    "\n",
+    "When working with large datasets, we need to provide a single DataFrame containing the input timesteps of all the timeseries for which wish to generate predictions. If we have future exogenous features, we should also include the future values of these features in the separate `futr_df` DataFrame. \n",
+    "\n",
+    "For the below prediction we are assuming we only want to predict the next 12 timesteps for Airline2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_df = valid[valid['id_col'] == 'Airline2']\n",
+    "# we set input_size=60 and horizon=12 when fitting the model\n",
+    "pred_df = valid_df[:60]\n",
+    "futr_df = valid_df[60:72]\n",
+    "futr_df = futr_df.drop([\"y\"], axis=1)\n",
+    "\n",
+    "predictions = nf.predict(df=pred_df, futr_df=futr_df, static_df=static)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
+    "# only select those from NHITS to show\n",
+    "preds = preds.reset_index(drop=True)\n",
+    "dropped = [x for x in preds.columns if 'StemGNN' in x or 'RNN' in x]\n",
+    "preds = preds.drop(columns=dropped)\n",
+    "\n",
+    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
+    "\n",
+    "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
+    "\n",
+    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
+    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
+    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
+    "ax.legend(prop={'size': 15})\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Caveat\n",
+    "\n",
+    "One caveat to note is that we do not support the conformalize quantiled prediction outputs computed by loss functions such as\n",
+    " * [MQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#multi-quantile-loss-mqloss)\n",
+    " * [DistributionLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#distributionloss)\n",
+    " * [PMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#poisson-mixture-mesh-pmm)\n",
+    " * [GMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#gaussian-mixture-mesh-gmm)\n",
+    " * [NBMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#negative-binomial-mixture-mesh-nbmm)\n",
+    " * [HuberQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#huberized-quantile-loss)\n",
+    " * [HuberMQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#huberized-mqloss)\n",
+    " * [QuantileLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#quantile-loss)\n",
+    " * [IQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#implicit-quantile-loss-iqloss)\n",
+    " * [sCRPS](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#scaled-continuous-ranked-probability-score-scrps)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From acd4f9cafb58bac3fcb3a8cf34bbe702b0e51ab3 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 15:10:17 +0000
Subject: [PATCH 11/25] Fix attribute error during model saving

---
 nbs/core.ipynb         | 566 ++++++++++++++++++++++++++++++++++++++++-
 neuralforecast/core.py |  10 +-
 2 files changed, 563 insertions(+), 13 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 79b27a963..7e967f09b 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -15,7 +15,16 @@
    "execution_count": null,
    "id": "15392f6f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "%load_ext autoreload\n",
@@ -557,6 +566,7 @@
     "                raise Exception('Set val_size>0 if early stopping is enabled.')\n",
     "        \n",
     "        self._cs_df: Optional[DataFrame] = None\n",
+    "        self.conformal_intervals: Optional[ConformalIntervals] = None\n",
     "\n",
     "        # Process and save new dataset (in self)\n",
     "        if isinstance(df, (pd.DataFrame, pl_DataFrame)):\n",
@@ -1017,9 +1027,9 @@
     "\n",
     "        # perform conformal predictions\n",
     "        if conformal_level is not None:\n",
-    "            if self._cs_df is None:\n",
+    "            if self._cs_df is None or self.conformal_intervals is None:\n",
     "                warn_msg = (\n",
-    "                    'Please rerun the `fit` method passing a valid confrmal_interval settings to compute conformity scores'\n",
+    "                    'Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores'\n",
     "                )\n",
     "                warnings.warn(warn_msg, UserWarning)\n",
     "            else:\n",
@@ -1657,6 +1667,9 @@
     "        target_col: str = 'y',\n",
     "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
     "        \"\"\"\n",
+    "        if self.conformal_intervals is None:\n",
+    "            raise AttributeError('Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores')\n",
+    "                    \n",
     "        min_size = ufp.counts_by_id(df, id_col)['counts'].min()\n",
     "        min_samples = self.h * self.conformal_intervals.n_windows + 1\n",
     "        if min_size < min_samples:\n",
@@ -1716,7 +1729,95 @@
    "execution_count": null,
    "id": "4bede563-78c0-40ee-ba76-f06f329cd772",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L431){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.fit\n",
+       "\n",
+       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
+       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
+       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
+       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
+       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
+       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
+       ">                          use_init_models:bool=False, verbose:bool=False,\n",
+       ">                          id_col:str='unique_id', time_col:str='ds',\n",
+       ">                          target_col:str='y', distributed_config:Optional[neura\n",
+       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
+       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
+       ">                          formalIntervals]=None)\n",
+       "\n",
+       "*Fit the core.NeuralForecast.\n",
+       "\n",
+       "Fit `models` to a large set of time series from DataFrame `df`.\n",
+       "and store fitted models for later inspection.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| val_size | Optional | 0 | Size of validation set. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
+       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
+       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L431){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.fit\n",
+       "\n",
+       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
+       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
+       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
+       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
+       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
+       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
+       ">                          use_init_models:bool=False, verbose:bool=False,\n",
+       ">                          id_col:str='unique_id', time_col:str='ds',\n",
+       ">                          target_col:str='y', distributed_config:Optional[neura\n",
+       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
+       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
+       ">                          formalIntervals]=None)\n",
+       "\n",
+       "*Fit the core.NeuralForecast.\n",
+       "\n",
+       "Fit `models` to a large set of time series from DataFrame `df`.\n",
+       "and store fitted models for later inspection.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| val_size | Optional | 0 | Size of validation set. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
+       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
+       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.fit, title_level=3)"
    ]
@@ -1726,7 +1827,85 @@
    "execution_count": null,
    "id": "f90209f6-16da-40a6-8302-1c5c2f66c619",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict\n",
+       "\n",
+       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
+       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
+       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
+       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
+       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
+       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
+       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
+       ">                              compat.SparkDataFrame,NoneType]=None,\n",
+       ">                              sort_df:bool=True, verbose:bool=False,\n",
+       ">                              engine=None, conformal_level:Optional[List[Union[\n",
+       ">                              int,float]]]=None, **data_kwargs)\n",
+       "\n",
+       "*Predict with core.NeuralForecast.\n",
+       "\n",
+       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
+       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict\n",
+       "\n",
+       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
+       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
+       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
+       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
+       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
+       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
+       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
+       ">                              compat.SparkDataFrame,NoneType]=None,\n",
+       ">                              sort_df:bool=True, verbose:bool=False,\n",
+       ">                              engine=None, conformal_level:Optional[List[Union[\n",
+       ">                              int,float]]]=None, **data_kwargs)\n",
+       "\n",
+       "*Predict with core.NeuralForecast.\n",
+       "\n",
+       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
+       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.predict, title_level=3)"
    ]
@@ -1736,7 +1915,107 @@
    "execution_count": null,
    "id": "19a8923a-f4f3-4e60-b9b9-a7088fc9bff5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1125){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.cross_validation\n",
+       "\n",
+       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
+       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
+       ">                                       None, static_df:Union[pandas.core.frame.\n",
+       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
+       ">                                       me,NoneType]=None, n_windows:int=1,\n",
+       ">                                       step_size:int=1,\n",
+       ">                                       val_size:Optional[int]=0,\n",
+       ">                                       test_size:Optional[int]=None,\n",
+       ">                                       sort_df:bool=True,\n",
+       ">                                       use_init_models:bool=False,\n",
+       ">                                       verbose:bool=False,\n",
+       ">                                       refit:Union[bool,int]=False,\n",
+       ">                                       id_col:str='unique_id',\n",
+       ">                                       time_col:str='ds', target_col:str='y',\n",
+       ">                                       **data_kwargs)\n",
+       "\n",
+       "*Temporal Cross-Validation with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
+       "models through multiple windows, in either chained or rolled manner.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
+       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target.             |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1125){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.cross_validation\n",
+       "\n",
+       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
+       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
+       ">                                       None, static_df:Union[pandas.core.frame.\n",
+       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
+       ">                                       me,NoneType]=None, n_windows:int=1,\n",
+       ">                                       step_size:int=1,\n",
+       ">                                       val_size:Optional[int]=0,\n",
+       ">                                       test_size:Optional[int]=None,\n",
+       ">                                       sort_df:bool=True,\n",
+       ">                                       use_init_models:bool=False,\n",
+       ">                                       verbose:bool=False,\n",
+       ">                                       refit:Union[bool,int]=False,\n",
+       ">                                       id_col:str='unique_id',\n",
+       ">                                       time_col:str='ds', target_col:str='y',\n",
+       ">                                       **data_kwargs)\n",
+       "\n",
+       "*Temporal Cross-Validation with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
+       "models through multiple windows, in either chained or rolled manner.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
+       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target.             |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.cross_validation, title_level=3)"
    ]
@@ -1746,7 +2025,53 @@
    "execution_count": null,
    "id": "355df52b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1285){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict_insample\n",
+       "\n",
+       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
+       "\n",
+       "*Predict insample with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
+       "to predict historic values of a time series from the stored dataframe.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1285){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict_insample\n",
+       "\n",
+       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
+       "\n",
+       "*Predict insample with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
+       "to predict historic values of a time series from the stored dataframe.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.predict_insample, title_level=3)"
    ]
@@ -1756,7 +2081,61 @@
    "execution_count": null,
    "id": "93155738-b40f-43d3-ba76-d345bf2583d5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1410){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.save\n",
+       "\n",
+       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
+       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
+       "\n",
+       "*Save NeuralForecast core class.\n",
+       "\n",
+       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
+       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
+       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory to save current status. |\n",
+       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
+       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
+       "| overwrite | bool | False | Whether to overwrite files or not. |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1410){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.save\n",
+       "\n",
+       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
+       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
+       "\n",
+       "*Save NeuralForecast core class.\n",
+       "\n",
+       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
+       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
+       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory to save current status. |\n",
+       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
+       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
+       "| overwrite | bool | False | Whether to overwrite files or not. |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.save, title_level=3)"
    ]
@@ -1766,7 +2145,55 @@
    "execution_count": null,
    "id": "0e915796-173c-4400-812f-c6351d5df3be",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1518){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.load\n",
+       "\n",
+       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
+       "\n",
+       "*Load NeuralForecast\n",
+       "\n",
+       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory with stored artifacts. |\n",
+       "| verbose | bool | False |  |\n",
+       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
+       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1518){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.load\n",
+       "\n",
+       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
+       "\n",
+       "*Load NeuralForecast\n",
+       "\n",
+       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory with stored artifacts. |\n",
+       "| verbose | bool | False |  |\n",
+       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
+       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.load, title_level=3)"
    ]
@@ -1837,7 +2264,37 @@
    "execution_count": null,
    "id": "c596acd4-c95a-41f3-a710-cb9b2c27459d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s, v_num=728, train_loss_step=46.40, train_loss_epoch=46.40]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 223.17it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 12.75it/s]\n",
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.34it/s, v_num=731, train_loss_step=141.0, train_loss_epoch=141.0]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 281.52it/s]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[80], line 21\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(input_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     18\u001b[0m output_id_warnings \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     19\u001b[0m     w \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m issued_warnings \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe predictions will have the id as a column\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mmessage)\n\u001b[1;32m     20\u001b[0m ]\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "# id as index warnings\n",
@@ -2593,7 +3050,94 @@
    "execution_count": null,
    "id": "6f61d030-a51e-49f8-a16e-b97bccb62401",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[36m(_train_tune pid=76328)\u001b[0m -----------------------------------------------\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(_train_tune pid=76328)\u001b[0m 1 | padder_train | ConstantPad1d | 0     \n",
+      "\u001b[36m(_train_tune pid=76328)\u001b[0m 3 | mlp          | ModuleList    | 72.2 K\n",
+      "\u001b[36m(_train_tune pid=76328)\u001b[0m 4 | out          | Linear        | 3.1 K \n",
+      "\u001b[36m(_train_tune pid=78699)\u001b[0m 1 | padder_train | ConstantPad1d | 0     \n",
+      "\u001b[36m(_train_tune pid=78699)\u001b[0m 3 | mlp          | ModuleList    | 72.2 K\n",
+      "\u001b[36m(_train_tune pid=78699)\u001b[0m 4 | out          | Linear        | 3.1 K \n",
+      "2024-10-03 13:53:49,549\tWARNING experiment_state.py:205 -- Experiment state snapshotting has been triggered multiple times in the last 5.0 seconds. A snapshot is forced if `CheckpointConfig(num_to_keep)` is set, and a trial has checkpointed >= `num_to_keep` times since the last snapshot.\n",
+      "You may want to consider increasing the `CheckpointConfig(num_to_keep)` or decreasing the frequency of saving checkpoints.\n",
+      "You can suppress this error by setting the environment variable TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S to a smaller value than the current threshold (5.0).\n",
+      "2024-10-03 13:53:49,550\tINFO tune.py:1007 -- Wrote the latest version of all result files and experiment state to '/root/ray_results/_train_tune_2024-10-03_13-53-46' in 0.0037s.\n",
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00, 12.73it/s, v_num=745, train_loss_step=440.0, train_loss_epoch=440.0]\n",
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.31it/s, v_num=746, train_loss_step=104.0, train_loss_epoch=104.0]\n",
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  5.10it/s, v_num=747, train_loss_step=2.630, train_loss_epoch=2.630]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 106.57it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00,  3.65it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 703.39it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 43.58it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 215.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n",
+      "Seed set to 1\n",
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unable to locate credentials\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n",
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 37.08it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 154.09it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 112.36it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 162.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33m(raylet)\u001b[0m [2024-10-03 14:36:33,691 E 72913 72913] (raylet) node_manager.cc:2963: 6 Workers (tasks / actors) killed due to memory pressure (OOM), 0 Workers crashed due to other reasons at node (ID: d5bd6836bacba6feb8e0a07e3fca742e76fb035ef63101dd5eb32b7d, IP: 172.17.0.2) over the last time period. To see more information about the Workers killed on this node, use `ray logs raylet.out -ip 172.17.0.2`\n",
+      "\u001b[33m(raylet)\u001b[0m \n",
+      "\u001b[33m(raylet)\u001b[0m Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "# test save and load\n",
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index c3677d827..d006e1a94 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -489,6 +489,7 @@ def fit(
             raise Exception("Set val_size>0 if early stopping is enabled.")
 
         self._cs_df: Optional[DataFrame] = None
+        self.conformal_intervals: Optional[ConformalIntervals] = None
 
         # Process and save new dataset (in self)
         if isinstance(df, (pd.DataFrame, pl_DataFrame)):
@@ -971,8 +972,8 @@ def predict(
 
         # perform conformal predictions
         if conformal_level is not None:
-            if self._cs_df is None:
-                warn_msg = "Please rerun the `fit` method passing a valid confrmal_interval settings to compute conformity scores"
+            if self._cs_df is None or self.conformal_intervals is None:
+                warn_msg = "Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores"
                 warnings.warn(warn_msg, UserWarning)
             else:
                 level_ = sorted(conformal_level)
@@ -1642,6 +1643,11 @@ def _conformity_scores(
         target_col: str = 'y',
         static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
         """
+        if self.conformal_intervals is None:
+            raise AttributeError(
+                "Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores"
+            )
+
         min_size = ufp.counts_by_id(df, id_col)["counts"].min()
         min_samples = self.h * self.conformal_intervals.n_windows + 1
         if min_size < min_samples:

From 0cf28029864d8a1e20cb16b9a3e47312798e5578 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 22:49:19 +0000
Subject: [PATCH 12/25] Review: clear core.ipynb output

---
 nbs/core.ipynb | 558 +------------------------------------------------
 1 file changed, 9 insertions(+), 549 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 7e967f09b..8a12d21be 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -15,16 +15,7 @@
    "execution_count": null,
    "id": "15392f6f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "%load_ext autoreload\n",
@@ -1729,95 +1720,7 @@
    "execution_count": null,
    "id": "4bede563-78c0-40ee-ba76-f06f329cd772",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L431){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.fit\n",
-       "\n",
-       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
-       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
-       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
-       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
-       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
-       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
-       ">                          use_init_models:bool=False, verbose:bool=False,\n",
-       ">                          id_col:str='unique_id', time_col:str='ds',\n",
-       ">                          target_col:str='y', distributed_config:Optional[neura\n",
-       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
-       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
-       ">                          formalIntervals]=None)\n",
-       "\n",
-       "*Fit the core.NeuralForecast.\n",
-       "\n",
-       "Fit `models` to a large set of time series from DataFrame `df`.\n",
-       "and store fitted models for later inspection.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| val_size | Optional | 0 | Size of validation set. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target. |\n",
-       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
-       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
-       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L431){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.fit\n",
-       "\n",
-       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
-       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
-       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
-       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
-       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
-       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
-       ">                          use_init_models:bool=False, verbose:bool=False,\n",
-       ">                          id_col:str='unique_id', time_col:str='ds',\n",
-       ">                          target_col:str='y', distributed_config:Optional[neura\n",
-       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
-       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
-       ">                          formalIntervals]=None)\n",
-       "\n",
-       "*Fit the core.NeuralForecast.\n",
-       "\n",
-       "Fit `models` to a large set of time series from DataFrame `df`.\n",
-       "and store fitted models for later inspection.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| val_size | Optional | 0 | Size of validation set. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target. |\n",
-       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
-       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
-       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.fit, title_level=3)"
    ]
@@ -1827,85 +1730,7 @@
    "execution_count": null,
    "id": "f90209f6-16da-40a6-8302-1c5c2f66c619",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict\n",
-       "\n",
-       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
-       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
-       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
-       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
-       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
-       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
-       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
-       ">                              compat.SparkDataFrame,NoneType]=None,\n",
-       ">                              sort_df:bool=True, verbose:bool=False,\n",
-       ">                              engine=None, conformal_level:Optional[List[Union[\n",
-       ">                              int,float]]]=None, **data_kwargs)\n",
-       "\n",
-       "*Predict with core.NeuralForecast.\n",
-       "\n",
-       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
-       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict\n",
-       "\n",
-       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
-       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
-       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
-       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
-       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
-       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
-       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
-       ">                              compat.SparkDataFrame,NoneType]=None,\n",
-       ">                              sort_df:bool=True, verbose:bool=False,\n",
-       ">                              engine=None, conformal_level:Optional[List[Union[\n",
-       ">                              int,float]]]=None, **data_kwargs)\n",
-       "\n",
-       "*Predict with core.NeuralForecast.\n",
-       "\n",
-       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
-       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.predict, title_level=3)"
    ]
@@ -1915,107 +1740,7 @@
    "execution_count": null,
    "id": "19a8923a-f4f3-4e60-b9b9-a7088fc9bff5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1125){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.cross_validation\n",
-       "\n",
-       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
-       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
-       ">                                       None, static_df:Union[pandas.core.frame.\n",
-       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
-       ">                                       me,NoneType]=None, n_windows:int=1,\n",
-       ">                                       step_size:int=1,\n",
-       ">                                       val_size:Optional[int]=0,\n",
-       ">                                       test_size:Optional[int]=None,\n",
-       ">                                       sort_df:bool=True,\n",
-       ">                                       use_init_models:bool=False,\n",
-       ">                                       verbose:bool=False,\n",
-       ">                                       refit:Union[bool,int]=False,\n",
-       ">                                       id_col:str='unique_id',\n",
-       ">                                       time_col:str='ds', target_col:str='y',\n",
-       ">                                       **data_kwargs)\n",
-       "\n",
-       "*Temporal Cross-Validation with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
-       "models through multiple windows, in either chained or rolled manner.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
-       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target.             |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1125){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.cross_validation\n",
-       "\n",
-       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
-       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
-       ">                                       None, static_df:Union[pandas.core.frame.\n",
-       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
-       ">                                       me,NoneType]=None, n_windows:int=1,\n",
-       ">                                       step_size:int=1,\n",
-       ">                                       val_size:Optional[int]=0,\n",
-       ">                                       test_size:Optional[int]=None,\n",
-       ">                                       sort_df:bool=True,\n",
-       ">                                       use_init_models:bool=False,\n",
-       ">                                       verbose:bool=False,\n",
-       ">                                       refit:Union[bool,int]=False,\n",
-       ">                                       id_col:str='unique_id',\n",
-       ">                                       time_col:str='ds', target_col:str='y',\n",
-       ">                                       **data_kwargs)\n",
-       "\n",
-       "*Temporal Cross-Validation with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
-       "models through multiple windows, in either chained or rolled manner.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
-       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target.             |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.cross_validation, title_level=3)"
    ]
@@ -2025,53 +1750,7 @@
    "execution_count": null,
    "id": "355df52b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1285){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict_insample\n",
-       "\n",
-       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
-       "\n",
-       "*Predict insample with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
-       "to predict historic values of a time series from the stored dataframe.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1285){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict_insample\n",
-       "\n",
-       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
-       "\n",
-       "*Predict insample with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
-       "to predict historic values of a time series from the stored dataframe.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.predict_insample, title_level=3)"
    ]
@@ -2081,61 +1760,7 @@
    "execution_count": null,
    "id": "93155738-b40f-43d3-ba76-d345bf2583d5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1410){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.save\n",
-       "\n",
-       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
-       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
-       "\n",
-       "*Save NeuralForecast core class.\n",
-       "\n",
-       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
-       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
-       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory to save current status. |\n",
-       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
-       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
-       "| overwrite | bool | False | Whether to overwrite files or not. |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1410){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.save\n",
-       "\n",
-       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
-       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
-       "\n",
-       "*Save NeuralForecast core class.\n",
-       "\n",
-       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
-       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
-       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory to save current status. |\n",
-       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
-       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
-       "| overwrite | bool | False | Whether to overwrite files or not. |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.save, title_level=3)"
    ]
@@ -2145,55 +1770,7 @@
    "execution_count": null,
    "id": "0e915796-173c-4400-812f-c6351d5df3be",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1518){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.load\n",
-       "\n",
-       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
-       "\n",
-       "*Load NeuralForecast\n",
-       "\n",
-       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory with stored artifacts. |\n",
-       "| verbose | bool | False |  |\n",
-       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
-       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1518){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.load\n",
-       "\n",
-       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
-       "\n",
-       "*Load NeuralForecast\n",
-       "\n",
-       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory with stored artifacts. |\n",
-       "| verbose | bool | False |  |\n",
-       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
-       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.load, title_level=3)"
    ]
@@ -2264,37 +1841,7 @@
    "execution_count": null,
    "id": "c596acd4-c95a-41f3-a710-cb9b2c27459d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s, v_num=728, train_loss_step=46.40, train_loss_epoch=46.40]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 223.17it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 12.75it/s]\n",
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.34it/s, v_num=731, train_loss_step=141.0, train_loss_epoch=141.0]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 281.52it/s]\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[80], line 21\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(input_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     18\u001b[0m output_id_warnings \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     19\u001b[0m     w \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m issued_warnings \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe predictions will have the id as a column\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mmessage)\n\u001b[1;32m     20\u001b[0m ]\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "# id as index warnings\n",
@@ -3050,94 +2597,7 @@
    "execution_count": null,
    "id": "6f61d030-a51e-49f8-a16e-b97bccb62401",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[36m(_train_tune pid=76328)\u001b[0m -----------------------------------------------\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
-      "\u001b[36m(_train_tune pid=76328)\u001b[0m 1 | padder_train | ConstantPad1d | 0     \n",
-      "\u001b[36m(_train_tune pid=76328)\u001b[0m 3 | mlp          | ModuleList    | 72.2 K\n",
-      "\u001b[36m(_train_tune pid=76328)\u001b[0m 4 | out          | Linear        | 3.1 K \n",
-      "\u001b[36m(_train_tune pid=78699)\u001b[0m 1 | padder_train | ConstantPad1d | 0     \n",
-      "\u001b[36m(_train_tune pid=78699)\u001b[0m 3 | mlp          | ModuleList    | 72.2 K\n",
-      "\u001b[36m(_train_tune pid=78699)\u001b[0m 4 | out          | Linear        | 3.1 K \n",
-      "2024-10-03 13:53:49,549\tWARNING experiment_state.py:205 -- Experiment state snapshotting has been triggered multiple times in the last 5.0 seconds. A snapshot is forced if `CheckpointConfig(num_to_keep)` is set, and a trial has checkpointed >= `num_to_keep` times since the last snapshot.\n",
-      "You may want to consider increasing the `CheckpointConfig(num_to_keep)` or decreasing the frequency of saving checkpoints.\n",
-      "You can suppress this error by setting the environment variable TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S to a smaller value than the current threshold (5.0).\n",
-      "2024-10-03 13:53:49,550\tINFO tune.py:1007 -- Wrote the latest version of all result files and experiment state to '/root/ray_results/_train_tune_2024-10-03_13-53-46' in 0.0037s.\n",
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00, 12.73it/s, v_num=745, train_loss_step=440.0, train_loss_epoch=440.0]\n",
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.31it/s, v_num=746, train_loss_step=104.0, train_loss_epoch=104.0]\n",
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  5.10it/s, v_num=747, train_loss_step=2.630, train_loss_epoch=2.630]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 106.57it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00,  3.65it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 703.39it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 43.58it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 215.87it/s]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 1\n",
-      "Seed set to 1\n",
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unable to locate credentials\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 1\n",
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 37.08it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 154.09it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 112.36it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 162.70it/s]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33m(raylet)\u001b[0m [2024-10-03 14:36:33,691 E 72913 72913] (raylet) node_manager.cc:2963: 6 Workers (tasks / actors) killed due to memory pressure (OOM), 0 Workers crashed due to other reasons at node (ID: d5bd6836bacba6feb8e0a07e3fca742e76fb035ef63101dd5eb32b7d, IP: 172.17.0.2) over the last time period. To see more information about the Workers killed on this node, use `ray logs raylet.out -ip 172.17.0.2`\n",
-      "\u001b[33m(raylet)\u001b[0m \n",
-      "\u001b[33m(raylet)\u001b[0m Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "# test save and load\n",

From 5eec5859c741446f84541f3da7ce639c9d003327 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 23:03:03 +0000
Subject: [PATCH 13/25] Review: Corrections to undesired copy-and-paste notes;
 revision of wordings

---
 .../tutorials/20_conformal_prediction.ipynb   | 141 +++++++-----------
 1 file changed, 58 insertions(+), 83 deletions(-)

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 0e7589923..6b14b9fa3 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -12,9 +12,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Conformal prediction intervals use cross-validation on a point forecaster model to generate the intervals. This means that no prior probabilities are needed, and the output is well-calibrated. No additional training is needed, and the model is treated as a black box. The approach is compatible with any model.\n",
+    "Conformal prediction uses cross-validation on a model trained with a point loss function to generate prediction intervals. No additional training is needed, and the model is treated as a black box. The approach is compatible with any model.\n",
     "\n",
-    "In this notebook, we demonstrate how to obtain intervals using the conformal prediction methods."
+    "In this notebook, we demonstrate how to obtain prediction intervals using conformal prediction."
    ]
   },
   {
@@ -32,15 +32,14 @@
    "source": [
     "import logging\n",
     "import os\n",
-    "import tempfile\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import NHITS\n",
-    "from utilsforecast.losses import mae, rmse, smape\n",
-    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic"
+    "from neuralforecast.utils import AirPassengersPanel\n",
+    "from neuralforecast.utils import ConformalIntervals"
    ]
   },
   {
@@ -59,7 +58,7 @@
    "source": [
     "## Data\n",
     "\n",
-    "We simply use AirPassengers dataset for the demostration of conformal predictions.\n"
+    "We simply use the AirPassengers dataset for the demonstration of conformal prediction.\n"
    ]
   },
   {
@@ -74,78 +73,49 @@
     "AirPassengersPanel_test['y_[lag12]'] = np.nan"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also create this directory structure with a spark dataframe using the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "spark.conf.set(\"spark.sql.parquet.outputTimestampType\", \"TIMESTAMP_MICROS\")\n",
-    "(\n",
-    "  spark_df\n",
-    "  .repartition(id_col)\n",
-    "  .sortWithinPartitions(id_col, time_col)\n",
-    "  .write\n",
-    "  .partitionBy(id_col)\n",
-    "  .parquet(out_dir)\n",
-    ")\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The DataLoader class still expects the static data to be passed in as a single DataFrame with one row per timeseries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "static = AirPassengersStatic.rename(columns={'unique_id': 'id_col'})\n",
-    "static"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Model training\n",
     "\n",
-    "We now train a NHITS model on the above dataset. \n",
-    "It is worth noting that NeuralForecast currently does not support scaling when using this DataLoader. If you want to scale the timeseries this should be done before passing it in to the `fit` method."
+    "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `ConformalIntervals` class and pass this to the `fit` method. By default, `ConformalIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. \n",
+    "\n",
+    "<br>\n",
+    "By default, `ConformalIntervas` class employs `method=conformal_distribution` for the conformal predictions. `method=conformal_error` is also supported. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` calculates quantiles directly from errors.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.39it/s, v_num=11, train_loss_step=14.40, train_loss_epoch=14.40]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 143.68it/s]\n",
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.22it/s, v_num=13, train_loss_step=10.30, train_loss_epoch=10.30]\n"
+     ]
+    }
+   ],
    "source": [
     "horizon = 12\n",
-    "stacks = 3\n",
-    "models = [NHITS(input_size=5 * horizon,\n",
-    "                h=horizon,\n",
-    "                futr_exog_list=['trend', 'y_[lag12]'],\n",
-    "                stat_exog_list=['airline1', 'airline2'],\n",
-    "                max_steps=100,\n",
-    "                stack_types = stacks*['identity'],\n",
-    "                n_blocks = stacks*[1],\n",
-    "                mlp_units = [[256,256] for _ in range(stacks)],\n",
-    "                n_pool_kernel_size = stacks*[1])]\n",
+    "input_size = 24\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals()\n",
+    "\n",
+    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100)]\n",
     "nf = NeuralForecast(models=models, freq='ME')\n",
-    "nf.fit(df=files_list, static_df=static, id_col='id_col')"
+    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)"
    ]
   },
   {
@@ -154,39 +124,44 @@
    "source": [
     "## Forecasting\n",
     "\n",
-    "When working with large datasets, we need to provide a single DataFrame containing the input timesteps of all the timeseries for which wish to generate predictions. If we have future exogenous features, we should also include the future values of these features in the separate `futr_df` DataFrame. \n",
-    "\n",
-    "For the below prediction we are assuming we only want to predict the next 12 timesteps for Airline2."
+    "To generate conformal intervals, we specify the desired levels in the `predict` method."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 217.33it/s]\n"
+     ]
+    }
+   ],
    "source": [
-    "valid_df = valid[valid['id_col'] == 'Airline2']\n",
-    "# we set input_size=60 and horizon=12 when fitting the model\n",
-    "pred_df = valid_df[:60]\n",
-    "futr_df = valid_df[60:72]\n",
-    "futr_df = futr_df.drop([\"y\"], axis=1)\n",
-    "\n",
-    "predictions = nf.predict(df=pred_df, futr_df=futr_df, static_df=static)"
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#| hide\n",
     "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
-    "# only select those from NHITS to show\n",
-    "preds = preds.reset_index(drop=True)\n",
-    "dropped = [x for x in preds.columns if 'StemGNN' in x or 'RNN' in x]\n",
-    "preds = preds.drop(columns=dropped)\n",
-    "\n",
     "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
     "\n",
     "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
@@ -214,7 +189,7 @@
     " * [HuberMQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#huberized-mqloss)\n",
     " * [QuantileLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#quantile-loss)\n",
     " * [IQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#implicit-quantile-loss-iqloss)\n",
-    " * [sCRPS](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#scaled-continuous-ranked-probability-score-scrps)"
+    " * [sCRPS](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#scaled-continuous-ranked-probability-score-scrps)\n"
    ]
   }
  ],

From d514058987317dab7f3ac2d1f55fa9759aeeb44d Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Thu, 3 Oct 2024 23:50:03 +0000
Subject: [PATCH 14/25] Improve example

Improve example
---
 .../tutorials/20_conformal_prediction.ipynb   | 79 ++++++++++++++++---
 1 file changed, 70 insertions(+), 9 deletions(-)

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 6b14b9fa3..1b7333dc9 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -38,8 +38,10 @@
     "import matplotlib.pyplot as plt\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import NHITS\n",
+    "from neuralforecast.models import MLP\n",
     "from neuralforecast.utils import AirPassengersPanel\n",
-    "from neuralforecast.utils import ConformalIntervals"
+    "from neuralforecast.utils import ConformalIntervals\n",
+    "from neuralforecast.losses.pytorch import DistributionLoss\n"
    ]
   },
   {
@@ -79,7 +81,7 @@
    "source": [
     "## Model training\n",
     "\n",
-    "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `ConformalIntervals` class and pass this to the `fit` method. By default, `ConformalIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. \n",
+    "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `ConformalIntervals` class and pass this to the `fit` method. By default, `ConformalIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. We also train a MLP model using DistributionLoss to demonstate the difference between conformal prediction and quantiled outputs. \n",
     "\n",
     "<br>\n",
     "By default, `ConformalIntervas` class employs `method=conformal_distribution` for the conformal predictions. `method=conformal_error` is also supported. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` calculates quantiles directly from errors.\n"
@@ -94,6 +96,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Seed set to 1\n",
       "Seed set to 1\n"
      ]
     },
@@ -101,9 +104,58 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.39it/s, v_num=11, train_loss_step=14.40, train_loss_epoch=14.40]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 143.68it/s]\n",
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.22it/s, v_num=13, train_loss_step=10.30, train_loss_epoch=10.30]\n"
+      "                                                                             "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  1.86it/s, v_num=15, train_loss_step=14.40, train_loss_epoch=14.40]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 194.52it/s]\n",
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  6.30it/s, v_num=17, train_loss_step=6.420, train_loss_epoch=6.420]    \n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 19.88it/s]\n",
+      "                                                                            "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.28it/s, v_num=19, train_loss_step=10.30, train_loss_epoch=10.30]\n",
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  4.12it/s, v_num=20, train_loss_step=3.09e+3, train_loss_epoch=3.09e+3]\n"
      ]
     }
    ],
@@ -113,7 +165,7 @@
     "\n",
     "conformal_intervals = ConformalIntervals()\n",
     "\n",
-    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100)]\n",
+    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100), MLP(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[10, 50, 90]))]\n",
     "nf = NeuralForecast(models=models, freq='ME')\n",
     "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)"
    ]
@@ -132,11 +184,19 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 217.33it/s]\n"
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 101.06it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 109.09it/s]\n"
      ]
     }
    ],
@@ -151,7 +211,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x700 with 1 Axes>"
       ]
@@ -162,7 +222,8 @@
    ],
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
-    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
+    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds').iloc[-200:]\n",
+    "\n",
     "\n",
     "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
     "\n",

From 8e4ab585577a367ce10fa0888c4dc7fbd1a77da9 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Fri, 4 Oct 2024 13:49:22 +0000
Subject: [PATCH 15/25] Review: Use DFType instead

fix
---
 nbs/utils.ipynb         | 14 +++++++-------
 neuralforecast/utils.py | 14 +++++++-------
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index 0352ed960..f372a8838 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -39,7 +39,7 @@
     "import random\n",
     "from itertools import chain\n",
     "from typing import List, Union\n",
-    "from utilsforecast.compat import DataFrame\n",
+    "from utilsforecast.compat import DFType\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -610,14 +610,14 @@
     "#| export\n",
     "\n",
     "def add_conformal_distribution_intervals(\n",
-    "    fcst_df: DataFrame, \n",
-    "    cs_df: DataFrame,\n",
+    "    fcst_df: DFType, \n",
+    "    cs_df: DFType,\n",
     "    model_names: List[str],\n",
     "    level: List[Union[int, float]],\n",
     "    cs_n_windows: int,\n",
     "    n_series: int,\n",
     "    horizon: int,\n",
-    ") -> DataFrame:\n",
+    ") -> DFType:\n",
     "    \"\"\"\n",
     "    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.\n",
     "    `level` should be already sorted. This strategy creates forecasts paths\n",
@@ -655,14 +655,14 @@
     "#| export\n",
     "\n",
     "def add_conformal_error_intervals(\n",
-    "    fcst_df: DataFrame, \n",
-    "    cs_df: DataFrame, \n",
+    "    fcst_df: DFType, \n",
+    "    cs_df: DFType, \n",
     "    model_names: List[str],\n",
     "    level: List[Union[int, float]],\n",
     "    cs_n_windows: int,\n",
     "    n_series: int,\n",
     "    horizon: int,\n",
-    ") -> DataFrame:\n",
+    ") -> DFType:\n",
     "    \"\"\"\n",
     "    Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.\n",
     "    `level` should be already sorted. This startegy creates prediction intervals\n",
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index bce62c60f..e18182d37 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -11,7 +11,7 @@
 import random
 from itertools import chain
 from typing import List, Union
-from utilsforecast.compat import DataFrame
+from utilsforecast.compat import DFType
 
 import numpy as np
 import pandas as pd
@@ -481,14 +481,14 @@ def __repr__(self):
 
 # %% ../nbs/utils.ipynb 32
 def add_conformal_distribution_intervals(
-    fcst_df: DataFrame,
-    cs_df: DataFrame,
+    fcst_df: DFType,
+    cs_df: DFType,
     model_names: List[str],
     level: List[Union[int, float]],
     cs_n_windows: int,
     n_series: int,
     horizon: int,
-) -> DataFrame:
+) -> DFType:
     """
     Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.
     `level` should be already sorted. This strategy creates forecasts paths
@@ -518,14 +518,14 @@ def add_conformal_distribution_intervals(
 
 # %% ../nbs/utils.ipynb 33
 def add_conformal_error_intervals(
-    fcst_df: DataFrame,
-    cs_df: DataFrame,
+    fcst_df: DFType,
+    cs_df: DFType,
     model_names: List[str],
     level: List[Union[int, float]],
     cs_n_windows: int,
     n_series: int,
     horizon: int,
-) -> DataFrame:
+) -> DFType:
     """
     Adds conformal intervals to a `fcst_df` based on conformal scores `cs_df`.
     `level` should be already sorted. This startegy creates prediction intervals

From 5503b6e64df042d46f6686fe6361a25b92e09a60 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Fri, 4 Oct 2024 14:25:13 +0000
Subject: [PATCH 16/25] Improve example with better illustration

---
 .../tutorials/20_conformal_prediction.ipynb   | 62 ++++++++++---------
 1 file changed, 33 insertions(+), 29 deletions(-)

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 1b7333dc9..8227b58b5 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -38,7 +38,7 @@
     "import matplotlib.pyplot as plt\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import NHITS\n",
-    "from neuralforecast.models import MLP\n",
+    "from neuralforecast.models import NLinear\n",
     "from neuralforecast.utils import AirPassengersPanel\n",
     "from neuralforecast.utils import ConformalIntervals\n",
     "from neuralforecast.losses.pytorch import DistributionLoss\n"
@@ -111,8 +111,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
       "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
@@ -120,14 +120,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  1.86it/s, v_num=15, train_loss_step=14.40, train_loss_epoch=14.40]"
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.48it/s, v_num=31, train_loss_step=14.40, train_loss_epoch=14.40]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
      ]
     },
     {
@@ -135,18 +135,18 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 194.52it/s]\n",
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  6.30it/s, v_num=17, train_loss_step=6.420, train_loss_epoch=6.420]    \n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 19.88it/s]\n",
-      "                                                                            "
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 204.83it/s]\n",
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.30it/s, v_num=33, train_loss_step=5.830, train_loss_epoch=5.830]   \n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 55.94it/s]\n",
+      "                                                                             "
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
       "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
@@ -154,8 +154,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.28it/s, v_num=19, train_loss_step=10.30, train_loss_epoch=10.30]\n",
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  4.12it/s, v_num=20, train_loss_step=3.09e+3, train_loss_epoch=3.09e+3]\n"
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s, v_num=35, train_loss_step=10.30, train_loss_epoch=10.30]\n",
+      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.15it/s, v_num=36, train_loss_step=5.640, train_loss_epoch=5.640]   \n"
      ]
     }
    ],
@@ -165,7 +165,7 @@
     "\n",
     "conformal_intervals = ConformalIntervals()\n",
     "\n",
-    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100), MLP(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[10, 50, 90]))]\n",
+    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100), NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[10, 50, 90]))]\n",
     "nf = NeuralForecast(models=models, freq='ME')\n",
     "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)"
    ]
@@ -176,7 +176,7 @@
    "source": [
     "## Forecasting\n",
     "\n",
-    "To generate conformal intervals, we specify the desired levels in the `predict` method."
+    "To generate conformal intervals, we specify the desired levels in the `predict` method. Note that in the following example, predictions labeled by `NHITS1` refer to the predictions by model using `DistributionLoss` loss function, which compute the intervals in quantiled manner. Predictions labeled with 'MODEL-conformal-lo/hi-#' are computed by conformal predictions meanwhile those labeled with 'MODEL-lo/hi-#' are computed in quantiled manner."
    ]
   },
   {
@@ -188,15 +188,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 101.06it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 109.09it/s]\n"
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 173.71it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 161.85it/s]\n"
      ]
     }
    ],
@@ -211,9 +211,9 @@
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 2000x700 with 1 Axes>"
+       "<Figure size 2000x700 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -221,17 +221,21 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
-    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds').iloc[-200:]\n",
-    "\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize = (20, 7))\n",
+    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
     "\n",
-    "plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).plot(ax=ax, linewidth=2)\n",
+    "plot_df = plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).iloc[-50:]\n",
+    "plot_df.drop([x for x in plot_df.columns if 'NHITS1' in x], axis=1).plot(ax=ax1, linewidth=2)\n",
+    "plot_df.drop([x for x in plot_df.columns if 'NHITS-' in x or x == \"NHITS\"], axis=1).plot(ax=ax2, linewidth=2)\n",
     "\n",
-    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
-    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
-    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
-    "ax.legend(prop={'size': 15})\n",
-    "ax.grid()"
+    "ax1.set_title('AirPassengers Forecast', fontsize=18)\n",
+    "ax1.set_ylabel('Monthly Passengers', fontsize=15)\n",
+    "ax1.legend(prop={'size': 10})\n",
+    "ax1.grid()\n",
+    "ax2.set_ylabel('Monthly Passengers', fontsize=15)\n",
+    "ax2.set_xlabel('Timestamp [t]', fontsize=15)\n",
+    "ax2.legend(prop={'size': 10})\n",
+    "ax2.grid()\n"
    ]
   },
   {

From f497c54fe823169591c4500ea71eeda37442858e Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 7 Oct 2024 14:40:04 +0000
Subject: [PATCH 17/25] Review: Simply without using
 UNSUPORTED_LOSSED_CONFORMAL, add argument to conformaliz quantiles if desired

---
 nbs/core.ipynb                   | 534 ++++++++++++++++++++++++++++++-
 nbs/losses.pytorch.ipynb         |  40 ---
 nbs/utils.ipynb                  |   6 +
 neuralforecast/core.py           |  23 +-
 neuralforecast/losses/pytorch.py |  20 +-
 neuralforecast/utils.py          |   6 +
 6 files changed, 550 insertions(+), 79 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 8a12d21be..7d06f02fe 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -15,7 +15,16 @@
    "execution_count": null,
    "id": "15392f6f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "%load_ext autoreload\n",
@@ -96,8 +105,7 @@
     "    TimeMixer, KAN, RMoK\n",
     ")\n",
     "from neuralforecast.common._base_auto import BaseAuto, MockTrial\n",
-    "from neuralforecast.utils import ConformalIntervals, get_conformal_method\n",
-    "from neuralforecast.losses.pytorch import UNSUPPORTED_LOSSES_CONFORMAL"
+    "from neuralforecast.utils import ConformalIntervals, get_conformal_method"
    ]
   },
   {
@@ -727,12 +735,12 @@
     "\n",
     "        return futr_exog | set(hist_exog)\n",
     "    \n",
-    "    def _get_model_names(self, use_conformal=False) -> List[str]:\n",
+    "    def _get_model_names(self, conformal=False, conformalize_quantiles=False) -> List[str]:\n",
     "        names: List[str] = []\n",
     "        count_names = {'model': 0}\n",
     "        for model in self.models:\n",
-    "            # skip model for consideration of conformal prediction\n",
-    "            if use_conformal and isinstance(model.loss, UNSUPPORTED_LOSSES_CONFORMAL):\n",
+    "            if conformal and not conformalize_quantiles and model.loss.outputsize_multiplier > 1:\n",
+    "                # skip conformalize quantile outputs\n",
     "                continue\n",
     "\n",
     "            model_name = repr(model)\n",
@@ -1025,7 +1033,7 @@
     "                warnings.warn(warn_msg, UserWarning)\n",
     "            else:\n",
     "                level_ = sorted(conformal_level)\n",
-    "                model_names = self._get_model_names(use_conformal=True)\n",
+    "                model_names = self._get_model_names(conformal=True, conformalize_quantiles=self.conformal_intervals.conformalize_quantiles)\n",
     "                conformal_method = get_conformal_method(self.conformal_intervals.method)\n",
     "\n",
     "                fcsts_df = conformal_method(\n",
@@ -1681,7 +1689,7 @@
     "        \n",
     "        kept = [time_col, id_col, 'cutoff']\n",
     "        # conformity score for each model\n",
-    "        for model in self._get_model_names(use_conformal=True):\n",
+    "        for model in self._get_model_names(conformal=True, conformalize_quantiles=self.conformal_intervals.conformalize_quantiles):\n",
     "            kept.append(model)\n",
     "\n",
     "            # compute absolute error for each model\n",
@@ -1720,7 +1728,95 @@
    "execution_count": null,
    "id": "4bede563-78c0-40ee-ba76-f06f329cd772",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L430){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.fit\n",
+       "\n",
+       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
+       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
+       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
+       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
+       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
+       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
+       ">                          use_init_models:bool=False, verbose:bool=False,\n",
+       ">                          id_col:str='unique_id', time_col:str='ds',\n",
+       ">                          target_col:str='y', distributed_config:Optional[neura\n",
+       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
+       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
+       ">                          formalIntervals]=None)\n",
+       "\n",
+       "*Fit the core.NeuralForecast.\n",
+       "\n",
+       "Fit `models` to a large set of time series from DataFrame `df`.\n",
+       "and store fitted models for later inspection.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| val_size | Optional | 0 | Size of validation set. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
+       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
+       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L430){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.fit\n",
+       "\n",
+       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
+       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
+       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
+       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
+       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
+       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
+       ">                          use_init_models:bool=False, verbose:bool=False,\n",
+       ">                          id_col:str='unique_id', time_col:str='ds',\n",
+       ">                          target_col:str='y', distributed_config:Optional[neura\n",
+       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
+       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
+       ">                          formalIntervals]=None)\n",
+       "\n",
+       "*Fit the core.NeuralForecast.\n",
+       "\n",
+       "Fit `models` to a large set of time series from DataFrame `df`.\n",
+       "and store fitted models for later inspection.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| val_size | Optional | 0 | Size of validation set. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
+       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
+       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.fit, title_level=3)"
    ]
@@ -1730,7 +1826,85 @@
    "execution_count": null,
    "id": "f90209f6-16da-40a6-8302-1c5c2f66c619",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict\n",
+       "\n",
+       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
+       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
+       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
+       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
+       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
+       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
+       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
+       ">                              compat.SparkDataFrame,NoneType]=None,\n",
+       ">                              sort_df:bool=True, verbose:bool=False,\n",
+       ">                              engine=None, conformal_level:Optional[List[Union[\n",
+       ">                              int,float]]]=None, **data_kwargs)\n",
+       "\n",
+       "*Predict with core.NeuralForecast.\n",
+       "\n",
+       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
+       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict\n",
+       "\n",
+       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
+       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
+       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
+       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
+       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
+       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
+       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
+       ">                              compat.SparkDataFrame,NoneType]=None,\n",
+       ">                              sort_df:bool=True, verbose:bool=False,\n",
+       ">                              engine=None, conformal_level:Optional[List[Union[\n",
+       ">                              int,float]]]=None, **data_kwargs)\n",
+       "\n",
+       "*Predict with core.NeuralForecast.\n",
+       "\n",
+       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
+       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.predict, title_level=3)"
    ]
@@ -1740,7 +1914,107 @@
    "execution_count": null,
    "id": "19a8923a-f4f3-4e60-b9b9-a7088fc9bff5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1126){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.cross_validation\n",
+       "\n",
+       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
+       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
+       ">                                       None, static_df:Union[pandas.core.frame.\n",
+       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
+       ">                                       me,NoneType]=None, n_windows:int=1,\n",
+       ">                                       step_size:int=1,\n",
+       ">                                       val_size:Optional[int]=0,\n",
+       ">                                       test_size:Optional[int]=None,\n",
+       ">                                       sort_df:bool=True,\n",
+       ">                                       use_init_models:bool=False,\n",
+       ">                                       verbose:bool=False,\n",
+       ">                                       refit:Union[bool,int]=False,\n",
+       ">                                       id_col:str='unique_id',\n",
+       ">                                       time_col:str='ds', target_col:str='y',\n",
+       ">                                       **data_kwargs)\n",
+       "\n",
+       "*Temporal Cross-Validation with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
+       "models through multiple windows, in either chained or rolled manner.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
+       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target.             |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1126){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.cross_validation\n",
+       "\n",
+       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
+       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
+       ">                                       None, static_df:Union[pandas.core.frame.\n",
+       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
+       ">                                       me,NoneType]=None, n_windows:int=1,\n",
+       ">                                       step_size:int=1,\n",
+       ">                                       val_size:Optional[int]=0,\n",
+       ">                                       test_size:Optional[int]=None,\n",
+       ">                                       sort_df:bool=True,\n",
+       ">                                       use_init_models:bool=False,\n",
+       ">                                       verbose:bool=False,\n",
+       ">                                       refit:Union[bool,int]=False,\n",
+       ">                                       id_col:str='unique_id',\n",
+       ">                                       time_col:str='ds', target_col:str='y',\n",
+       ">                                       **data_kwargs)\n",
+       "\n",
+       "*Temporal Cross-Validation with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
+       "models through multiple windows, in either chained or rolled manner.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
+       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
+       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
+       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
+       "| sort_df | bool | True | Sort `df` before fitting. |\n",
+       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
+       "| verbose | bool | False | Print processing steps. |\n",
+       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
+       "| target_col | str | y | Column that contains the target.             |\n",
+       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
+       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.cross_validation, title_level=3)"
    ]
@@ -1750,7 +2024,53 @@
    "execution_count": null,
    "id": "355df52b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1286){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict_insample\n",
+       "\n",
+       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
+       "\n",
+       "*Predict insample with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
+       "to predict historic values of a time series from the stored dataframe.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1286){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.predict_insample\n",
+       "\n",
+       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
+       "\n",
+       "*Predict insample with core.NeuralForecast.\n",
+       "\n",
+       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
+       "to predict historic values of a time series from the stored dataframe.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| step_size | int | 1 | Step size between each window. |\n",
+       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.predict_insample, title_level=3)"
    ]
@@ -1760,7 +2080,61 @@
    "execution_count": null,
    "id": "93155738-b40f-43d3-ba76-d345bf2583d5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1411){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.save\n",
+       "\n",
+       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
+       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
+       "\n",
+       "*Save NeuralForecast core class.\n",
+       "\n",
+       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
+       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
+       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory to save current status. |\n",
+       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
+       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
+       "| overwrite | bool | False | Whether to overwrite files or not. |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1411){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.save\n",
+       "\n",
+       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
+       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
+       "\n",
+       "*Save NeuralForecast core class.\n",
+       "\n",
+       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
+       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
+       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory to save current status. |\n",
+       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
+       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
+       "| overwrite | bool | False | Whether to overwrite files or not. |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.save, title_level=3)"
    ]
@@ -1770,7 +2144,55 @@
    "execution_count": null,
    "id": "0e915796-173c-4400-812f-c6351d5df3be",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1520){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.load\n",
+       "\n",
+       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
+       "\n",
+       "*Load NeuralForecast\n",
+       "\n",
+       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory with stored artifacts. |\n",
+       "| verbose | bool | False |  |\n",
+       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
+       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1520){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "\n",
+       "### NeuralForecast.load\n",
+       "\n",
+       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
+       "\n",
+       "*Load NeuralForecast\n",
+       "\n",
+       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| path | str |  | Directory with stored artifacts. |\n",
+       "| verbose | bool | False |  |\n",
+       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
+       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "show_doc(NeuralForecast.load, title_level=3)"
    ]
@@ -1841,7 +2263,37 @@
    "execution_count": null,
    "id": "c596acd4-c95a-41f3-a710-cb9b2c27459d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.05it/s, v_num=19, train_loss_step=46.40, train_loss_epoch=46.40]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 196.87it/s]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 11.65it/s]\n",
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.54it/s, v_num=22, train_loss_step=141.0, train_loss_epoch=141.0]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 224.94it/s]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[68], line 21\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(input_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     18\u001b[0m output_id_warnings \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     19\u001b[0m     w \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m issued_warnings \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe predictions will have the id as a column\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mmessage)\n\u001b[1;32m     20\u001b[0m ]\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "# id as index warnings\n",
@@ -3408,7 +3860,7 @@
    "source": [
     "#| hide\n",
     "# test conformal prediction are not applied for models with quantiled-related loss\n",
-    "# only those supported losses shall export columns with '*-conformal-*'\n",
+    "# by default (ConformalIntervals.conformalize_quantiles=False)\n",
     "\n",
     "conformal_intervals = ConformalIntervals()\n",
     "\n",
@@ -3431,6 +3883,56 @@
     "]\n",
     "assert all([col in preds.columns for col in pred_cols])\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b980087",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.54it/s, v_num=28, train_loss_step=25.60, train_loss_epoch=25.60]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 115.71it/s]\n",
+      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.63it/s, v_num=30, train_loss_step=17.30, train_loss_epoch=17.30]\n",
+      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 189.68it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| hide\n",
+    "# test conformal predictions applied to quantiles if ConformalIntervals.conformalize_quantiles=True\n",
+    "\n",
+    "conformal_intervals = ConformalIntervals(conformalize_quantiles=True)\n",
+    "\n",
+    "nf = NeuralForecast(models=[NHITS(h=12, input_size=24, max_steps=1, loss=MQLoss(level=[80]))], freq='M')\n",
+    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])\n",
+    "\n",
+    "pred_cols = [\n",
+    "       'NHITS-median', 'NHITS-lo-80', 'NHITS-hi-80',\n",
+    "       'NHITS-median-conformal-lo-90', 'NHITS-median-conformal-lo-50',\n",
+    "       'NHITS-median-conformal-lo-10', 'NHITS-median-conformal-hi-10',\n",
+    "       'NHITS-median-conformal-hi-50', 'NHITS-median-conformal-hi-90',\n",
+    "       'NHITS-lo-80-conformal-lo-90', 'NHITS-lo-80-conformal-lo-50',\n",
+    "       'NHITS-lo-80-conformal-lo-10', 'NHITS-lo-80-conformal-hi-10',\n",
+    "       'NHITS-lo-80-conformal-hi-50', 'NHITS-lo-80-conformal-hi-90',\n",
+    "       'NHITS-hi-80-conformal-lo-90', 'NHITS-hi-80-conformal-lo-50',\n",
+    "       'NHITS-hi-80-conformal-lo-10', 'NHITS-hi-80-conformal-hi-10',\n",
+    "       'NHITS-hi-80-conformal-hi-50', 'NHITS-hi-80-conformal-hi-90'\n",
+    "]\n",
+    "\n",
+    "assert all([col in preds.columns for col in pred_cols])\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/nbs/losses.pytorch.ipynb b/nbs/losses.pytorch.ipynb
index dae834be5..efcff01a1 100644
--- a/nbs/losses.pytorch.ipynb
+++ b/nbs/losses.pytorch.ipynb
@@ -4361,46 +4361,6 @@
     "loss = mae(y=y, y_hat=y_hat, mask=mask)\n",
     "assert loss==(1/3), 'Should be 1/3'"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99ada497",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Scaled Continuous Ranked Probability Score (sCRPS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c66abf27",
-   "metadata": {},
-   "source": [
-    "## Unsupported losses for conformal prediction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c960d4db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| export\n",
-    "UNSUPPORTED_LOSSES_CONFORMAL = (\n",
-    "    MQLoss,\n",
-    "    DistributionLoss,\n",
-    "    PMM,\n",
-    "    GMM,\n",
-    "    NBMM,\n",
-    "    HuberQLoss,\n",
-    "    HuberMQLoss, \n",
-    "    QuantileLoss,\n",
-    "    IQLoss,\n",
-    "    sCRPS,\n",
-    ")"
-   ]
   }
  ],
  "metadata": {
diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index f372a8838..a1994bb80 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -579,6 +579,7 @@
     "        self,\n",
     "        n_windows: int = 2,\n",
     "        method: str = \"conformal_distribution\",\n",
+    "        conformalize_quantiles: bool = False,\n",
     "    ):\n",
     "        \"\"\" \n",
     "        n_windows : int\n",
@@ -586,6 +587,10 @@
     "        method : str, default is conformal_distribution\n",
     "            One of the supported methods for the computation of conformal prediction:\n",
     "            conformal_error or conformal_distribution\n",
+    "        conformalize_quantiles : bool, default is False\n",
+    "            If set to True, we shall conformalize quantiled outputs, e.g. prediction made \n",
+    "            with MQLoss(level=[80]) will be conformalized with the respective conformal\n",
+    "            levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').\n",
     "        \"\"\"\n",
     "        if n_windows < 2:\n",
     "            raise ValueError(\n",
@@ -596,6 +601,7 @@
     "            raise ValueError(f\"method must be one of {allowed_methods}\")\n",
     "        self.n_windows = n_windows\n",
     "        self.method = method\n",
+    "        self.conformalize_quantiles = conformalize_quantiles\n",
     "\n",
     "    def __repr__(self):\n",
     "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')\""
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index d006e1a94..c7e73dca9 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -70,7 +70,6 @@
 )
 from .common._base_auto import BaseAuto, MockTrial
 from .utils import ConformalIntervals, get_conformal_method
-from .losses.pytorch import UNSUPPORTED_LOSSES_CONFORMAL
 
 # %% ../nbs/core.ipynb 5
 # this disables warnings about the number of workers in the dataloaders
@@ -667,12 +666,18 @@ def _get_needed_exog(self):
 
         return futr_exog | set(hist_exog)
 
-    def _get_model_names(self, use_conformal=False) -> List[str]:
+    def _get_model_names(
+        self, conformal=False, conformalize_quantiles=False
+    ) -> List[str]:
         names: List[str] = []
         count_names = {"model": 0}
         for model in self.models:
-            # skip model for consideration of conformal prediction
-            if use_conformal and isinstance(model.loss, UNSUPPORTED_LOSSES_CONFORMAL):
+            if (
+                conformal
+                and not conformalize_quantiles
+                and model.loss.outputsize_multiplier > 1
+            ):
+                # skip conformalize quantile outputs
                 continue
 
             model_name = repr(model)
@@ -977,7 +982,10 @@ def predict(
                 warnings.warn(warn_msg, UserWarning)
             else:
                 level_ = sorted(conformal_level)
-                model_names = self._get_model_names(use_conformal=True)
+                model_names = self._get_model_names(
+                    conformal=True,
+                    conformalize_quantiles=self.conformal_intervals.conformalize_quantiles,
+                )
                 conformal_method = get_conformal_method(self.conformal_intervals.method)
 
                 fcsts_df = conformal_method(
@@ -1668,7 +1676,10 @@ def _conformity_scores(
 
         kept = [time_col, id_col, "cutoff"]
         # conformity score for each model
-        for model in self._get_model_names(use_conformal=True):
+        for model in self._get_model_names(
+            conformal=True,
+            conformalize_quantiles=self.conformal_intervals.conformalize_quantiles,
+        ):
             kept.append(model)
 
             # compute absolute error for each model
diff --git a/neuralforecast/losses/pytorch.py b/neuralforecast/losses/pytorch.py
index 908958a99..a65b1c532 100644
--- a/neuralforecast/losses/pytorch.py
+++ b/neuralforecast/losses/pytorch.py
@@ -1,9 +1,9 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/losses.pytorch.ipynb.
 
 # %% auto 0
-__all__ = ['UNSUPPORTED_LOSSES_CONFORMAL', 'BasePointLoss', 'MAE', 'MSE', 'RMSE', 'MAPE', 'SMAPE', 'MASE', 'relMSE',
-           'QuantileLoss', 'MQLoss', 'QuantileLayer', 'IQLoss', 'DistributionLoss', 'PMM', 'GMM', 'NBMM', 'HuberLoss',
-           'TukeyLoss', 'HuberQLoss', 'HuberMQLoss', 'Accuracy', 'sCRPS']
+__all__ = ['BasePointLoss', 'MAE', 'MSE', 'RMSE', 'MAPE', 'SMAPE', 'MASE', 'relMSE', 'QuantileLoss', 'MQLoss', 'QuantileLayer',
+           'IQLoss', 'DistributionLoss', 'PMM', 'GMM', 'NBMM', 'HuberLoss', 'TukeyLoss', 'HuberQLoss', 'HuberMQLoss',
+           'Accuracy', 'sCRPS']
 
 # %% ../../nbs/losses.pytorch.ipynb 4
 from typing import Optional, Union, Tuple
@@ -3089,17 +3089,3 @@ def __call__(
         unmean = torch.sum(mask)
         scrps = 2 * mql * unmean / (norm + 1e-5)
         return scrps
-
-# %% ../../nbs/losses.pytorch.ipynb 128
-UNSUPPORTED_LOSSES_CONFORMAL = (
-    MQLoss,
-    DistributionLoss,
-    PMM,
-    GMM,
-    NBMM,
-    HuberQLoss,
-    HuberMQLoss,
-    QuantileLoss,
-    IQLoss,
-    sCRPS,
-)
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index e18182d37..4e59670e7 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -458,6 +458,7 @@ def __init__(
         self,
         n_windows: int = 2,
         method: str = "conformal_distribution",
+        conformalize_quantiles: bool = False,
     ):
         """
         n_windows : int
@@ -465,6 +466,10 @@ def __init__(
         method : str, default is conformal_distribution
             One of the supported methods for the computation of conformal prediction:
             conformal_error or conformal_distribution
+        conformalize_quantiles : bool, default is False
+            If set to True, we shall conformalize quantiled outputs, e.g. prediction made
+            with MQLoss(level=[80]) will be conformalized with the respective conformal
+            levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').
         """
         if n_windows < 2:
             raise ValueError(
@@ -475,6 +480,7 @@ def __init__(
             raise ValueError(f"method must be one of {allowed_methods}")
         self.n_windows = n_windows
         self.method = method
+        self.conformalize_quantiles = conformalize_quantiles
 
     def __repr__(self):
         return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')"

From d714e51aba7bee5d29af3943f9bc441c0b319e8e Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 7 Oct 2024 14:41:49 +0000
Subject: [PATCH 18/25] Revise example with the remark on
 conformalize_quantiles argument

---
 .../tutorials/20_conformal_prediction.ipynb   | 26 ++++---------------
 1 file changed, 5 insertions(+), 21 deletions(-)

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 8227b58b5..345a69b97 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -84,7 +84,8 @@
     "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `ConformalIntervals` class and pass this to the `fit` method. By default, `ConformalIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. We also train a MLP model using DistributionLoss to demonstate the difference between conformal prediction and quantiled outputs. \n",
     "\n",
     "<br>\n",
-    "By default, `ConformalIntervas` class employs `method=conformal_distribution` for the conformal predictions. `method=conformal_error` is also supported. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` calculates quantiles directly from errors.\n"
+    "\n",
+    "By default, `ConformalIntervals` class employs method=conformal_distribution for the conformal predictions. `method=conformal_error` is also supported. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` calculates quantiles directly from errors.\n"
    ]
   },
   {
@@ -176,7 +177,9 @@
    "source": [
     "## Forecasting\n",
     "\n",
-    "To generate conformal intervals, we specify the desired levels in the `predict` method. Note that in the following example, predictions labeled by `NHITS1` refer to the predictions by model using `DistributionLoss` loss function, which compute the intervals in quantiled manner. Predictions labeled with 'MODEL-conformal-lo/hi-#' are computed by conformal predictions meanwhile those labeled with 'MODEL-lo/hi-#' are computed in quantiled manner."
+    "To generate conformal intervals, we specify the desired levels in the `predict` method. Note that in the following example, predictions labeled by `NHITS1` refer to the predictions by model using `DistributionLoss` loss function, which compute the intervals in quantiled manner. Predictions labeled with 'MODEL-conformal-lo/hi-#' are computed by conformal predictions meanwhile those labeled with 'MODEL-lo/hi-#' are computed in quantiled manner. \n",
+    "\n",
+    "If we want to conformalize quantiled outputs, we need to set `ConformalIntervals(conformalize_quantiles=True)`."
    ]
   },
   {
@@ -237,25 +240,6 @@
     "ax2.legend(prop={'size': 10})\n",
     "ax2.grid()\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Caveat\n",
-    "\n",
-    "One caveat to note is that we do not support the conformalize quantiled prediction outputs computed by loss functions such as\n",
-    " * [MQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#multi-quantile-loss-mqloss)\n",
-    " * [DistributionLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#distributionloss)\n",
-    " * [PMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#poisson-mixture-mesh-pmm)\n",
-    " * [GMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#gaussian-mixture-mesh-gmm)\n",
-    " * [NBMM](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#negative-binomial-mixture-mesh-nbmm)\n",
-    " * [HuberQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#huberized-quantile-loss)\n",
-    " * [HuberMQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#huberized-mqloss)\n",
-    " * [QuantileLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#quantile-loss)\n",
-    " * [IQLoss](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#implicit-quantile-loss-iqloss)\n",
-    " * [sCRPS](https://nixtlaverse.nixtla.io/neuralforecast/losses.pytorch.html#scaled-continuous-ranked-probability-score-scrps)\n"
-   ]
   }
  ],
  "metadata": {

From be38777f34c8659bc5dc08de82f7dcd1da9a26fb Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 7 Oct 2024 14:47:56 +0000
Subject: [PATCH 19/25] clean nbs/core.ipynb

---
 nbs/core.ipynb | 489 +------------------------------------------------
 1 file changed, 9 insertions(+), 480 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 7d06f02fe..9dc4db61b 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -15,16 +15,7 @@
    "execution_count": null,
    "id": "15392f6f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "%load_ext autoreload\n",
@@ -1728,95 +1719,7 @@
    "execution_count": null,
    "id": "4bede563-78c0-40ee-ba76-f06f329cd772",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L430){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.fit\n",
-       "\n",
-       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
-       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
-       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
-       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
-       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
-       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
-       ">                          use_init_models:bool=False, verbose:bool=False,\n",
-       ">                          id_col:str='unique_id', time_col:str='ds',\n",
-       ">                          target_col:str='y', distributed_config:Optional[neura\n",
-       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
-       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
-       ">                          formalIntervals]=None)\n",
-       "\n",
-       "*Fit the core.NeuralForecast.\n",
-       "\n",
-       "Fit `models` to a large set of time series from DataFrame `df`.\n",
-       "and store fitted models for later inspection.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| val_size | Optional | 0 | Size of validation set. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target. |\n",
-       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
-       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
-       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L430){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.fit\n",
-       "\n",
-       ">      NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
-       ">                          .frame.DataFrame,neuralforecast.compat.SparkDataFrame\n",
-       ">                          ,Sequence[str],NoneType]=None, static_df:Union[pandas\n",
-       ">                          .core.frame.DataFrame,polars.dataframe.frame.DataFram\n",
-       ">                          e,neuralforecast.compat.SparkDataFrame,NoneType]=None\n",
-       ">                          , val_size:Optional[int]=0, sort_df:bool=True,\n",
-       ">                          use_init_models:bool=False, verbose:bool=False,\n",
-       ">                          id_col:str='unique_id', time_col:str='ds',\n",
-       ">                          target_col:str='y', distributed_config:Optional[neura\n",
-       ">                          lforecast.common._base_model.DistributedConfig]=None,\n",
-       ">                          conformal_intervals:Optional[neuralforecast.utils.Con\n",
-       ">                          formalIntervals]=None)\n",
-       "\n",
-       "*Fit the core.NeuralForecast.\n",
-       "\n",
-       "Fit `models` to a large set of time series from DataFrame `df`.\n",
-       "and store fitted models for later inspection.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| val_size | Optional | 0 | Size of validation set. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when NeuralForecast object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target. |\n",
-       "| distributed_config | Optional | None | Configuration to use for DDP training. Currently only spark is supported. |\n",
-       "| conformal_intervals | Optional | None | Configuration to calibrate prediction intervals (Conformal Prediction).             |\n",
-       "| **Returns** | **NeuralForecast** |  | **Returns `NeuralForecast` class with fitted `models`.** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.fit, title_level=3)"
    ]
@@ -1826,85 +1729,7 @@
    "execution_count": null,
    "id": "f90209f6-16da-40a6-8302-1c5c2f66c619",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict\n",
-       "\n",
-       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
-       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
-       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
-       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
-       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
-       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
-       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
-       ">                              compat.SparkDataFrame,NoneType]=None,\n",
-       ">                              sort_df:bool=True, verbose:bool=False,\n",
-       ">                              engine=None, conformal_level:Optional[List[Union[\n",
-       ">                              int,float]]]=None, **data_kwargs)\n",
-       "\n",
-       "*Predict with core.NeuralForecast.\n",
-       "\n",
-       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
-       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L799){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict\n",
-       "\n",
-       ">      NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf\n",
-       ">                              rame.frame.DataFrame,neuralforecast.compat.SparkD\n",
-       ">                              ataFrame,NoneType]=None, static_df:Union[pandas.c\n",
-       ">                              ore.frame.DataFrame,polars.dataframe.frame.DataFr\n",
-       ">                              ame,neuralforecast.compat.SparkDataFrame,NoneType\n",
-       ">                              ]=None, futr_df:Union[pandas.core.frame.DataFrame\n",
-       ">                              ,polars.dataframe.frame.DataFrame,neuralforecast.\n",
-       ">                              compat.SparkDataFrame,NoneType]=None,\n",
-       ">                              sort_df:bool=True, verbose:bool=False,\n",
-       ">                              engine=None, conformal_level:Optional[List[Union[\n",
-       ">                              int,float]]]=None, **data_kwargs)\n",
-       "\n",
-       "*Predict with core.NeuralForecast.\n",
-       "\n",
-       "Use stored fitted `models` to predict large set of time series from DataFrame `df`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If a DataFrame is passed, it is used to generate forecasts. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| futr_df | Union | None | DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| engine | NoneType | None | Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe. |\n",
-       "| conformal_level | Optional | None | Confidence levels between 0 and 100 for conformal intervals. |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **pandas or polars DataFrame** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.predict, title_level=3)"
    ]
@@ -1914,107 +1739,7 @@
    "execution_count": null,
    "id": "19a8923a-f4f3-4e60-b9b9-a7088fc9bff5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1126){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.cross_validation\n",
-       "\n",
-       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
-       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
-       ">                                       None, static_df:Union[pandas.core.frame.\n",
-       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
-       ">                                       me,NoneType]=None, n_windows:int=1,\n",
-       ">                                       step_size:int=1,\n",
-       ">                                       val_size:Optional[int]=0,\n",
-       ">                                       test_size:Optional[int]=None,\n",
-       ">                                       sort_df:bool=True,\n",
-       ">                                       use_init_models:bool=False,\n",
-       ">                                       verbose:bool=False,\n",
-       ">                                       refit:Union[bool,int]=False,\n",
-       ">                                       id_col:str='unique_id',\n",
-       ">                                       time_col:str='ds', target_col:str='y',\n",
-       ">                                       **data_kwargs)\n",
-       "\n",
-       "*Temporal Cross-Validation with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
-       "models through multiple windows, in either chained or rolled manner.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
-       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target.             |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1126){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.cross_validation\n",
-       "\n",
-       ">      NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol\n",
-       ">                                       ars.dataframe.frame.DataFrame,NoneType]=\n",
-       ">                                       None, static_df:Union[pandas.core.frame.\n",
-       ">                                       DataFrame,polars.dataframe.frame.DataFra\n",
-       ">                                       me,NoneType]=None, n_windows:int=1,\n",
-       ">                                       step_size:int=1,\n",
-       ">                                       val_size:Optional[int]=0,\n",
-       ">                                       test_size:Optional[int]=None,\n",
-       ">                                       sort_df:bool=True,\n",
-       ">                                       use_init_models:bool=False,\n",
-       ">                                       verbose:bool=False,\n",
-       ">                                       refit:Union[bool,int]=False,\n",
-       ">                                       id_col:str='unique_id',\n",
-       ">                                       time_col:str='ds', target_col:str='y',\n",
-       ">                                       **data_kwargs)\n",
-       "\n",
-       "*Temporal Cross-Validation with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s cross-validation efficiently fits a list of NeuralForecast \n",
-       "models through multiple windows, in either chained or rolled manner.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| df | Union | None | DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables.<br>If None, a previously stored dataset is required. |\n",
-       "| static_df | Union | None | DataFrame with columns [`unique_id`] and static exogenous. |\n",
-       "| n_windows | int | 1 | Number of windows used for cross validation. |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| val_size | Optional | 0 | Length of validation size. If passed, set `n_windows=None`. |\n",
-       "| test_size | Optional | None | Length of test size. If passed, set `n_windows=None`. |\n",
-       "| sort_df | bool | True | Sort `df` before fitting. |\n",
-       "| use_init_models | bool | False | Use initial model passed when object was instantiated. |\n",
-       "| verbose | bool | False | Print processing steps. |\n",
-       "| refit | Union | False | Retrain model for each cross validation window.<br>If False, the models are trained at the beginning and then used to predict each window.<br>If positive int, the models are retrained every `refit` windows. |\n",
-       "| id_col | str | unique_id | Column that identifies each serie. |\n",
-       "| time_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |\n",
-       "| target_col | str | y | Column that contains the target.             |\n",
-       "| data_kwargs | kwargs |  | Extra arguments to be passed to the dataset within each model. |\n",
-       "| **Returns** | **Union** |  | **DataFrame with insample `models` columns for point predictions and probabilistic<br>predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.cross_validation, title_level=3)"
    ]
@@ -2024,53 +1749,7 @@
    "execution_count": null,
    "id": "355df52b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1286){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict_insample\n",
-       "\n",
-       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
-       "\n",
-       "*Predict insample with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
-       "to predict historic values of a time series from the stored dataframe.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1286){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.predict_insample\n",
-       "\n",
-       ">      NeuralForecast.predict_insample (step_size:int=1)\n",
-       "\n",
-       "*Predict insample with core.NeuralForecast.\n",
-       "\n",
-       "`core.NeuralForecast`'s `predict_insample` uses stored fitted `models`\n",
-       "to predict historic values of a time series from the stored dataframe.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| step_size | int | 1 | Step size between each window. |\n",
-       "| **Returns** | **pandas.DataFrame** |  | **DataFrame with insample predictions for all fitted `models`.    ** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.predict_insample, title_level=3)"
    ]
@@ -2080,61 +1759,7 @@
    "execution_count": null,
    "id": "93155738-b40f-43d3-ba76-d345bf2583d5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1411){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.save\n",
-       "\n",
-       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
-       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
-       "\n",
-       "*Save NeuralForecast core class.\n",
-       "\n",
-       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
-       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
-       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory to save current status. |\n",
-       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
-       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
-       "| overwrite | bool | False | Whether to overwrite files or not. |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1411){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.save\n",
-       "\n",
-       ">      NeuralForecast.save (path:str, model_index:Optional[List]=None,\n",
-       ">                           save_dataset:bool=True, overwrite:bool=False)\n",
-       "\n",
-       "*Save NeuralForecast core class.\n",
-       "\n",
-       "`core.NeuralForecast`'s method to save current status of models, dataset, and configuration.\n",
-       "Note that by default the `models` are not saving training checkpoints to save disk memory,\n",
-       "to get them change the individual model `**trainer_kwargs` to include `enable_checkpointing=True`.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory to save current status. |\n",
-       "| model_index | Optional | None | List to specify which models from list of self.models to save. |\n",
-       "| save_dataset | bool | True | Whether to save dataset or not. |\n",
-       "| overwrite | bool | False | Whether to overwrite files or not. |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.save, title_level=3)"
    ]
@@ -2144,55 +1769,7 @@
    "execution_count": null,
    "id": "0e915796-173c-4400-812f-c6351d5df3be",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1520){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.load\n",
-       "\n",
-       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
-       "\n",
-       "*Load NeuralForecast\n",
-       "\n",
-       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory with stored artifacts. |\n",
-       "| verbose | bool | False |  |\n",
-       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
-       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
-      ],
-      "text/plain": [
-       "---\n",
-       "\n",
-       "[source](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/core.py#L1520){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
-       "\n",
-       "### NeuralForecast.load\n",
-       "\n",
-       ">      NeuralForecast.load (path, verbose=False, **kwargs)\n",
-       "\n",
-       "*Load NeuralForecast\n",
-       "\n",
-       "`core.NeuralForecast`'s method to load checkpoint from path.*\n",
-       "\n",
-       "|    | **Type** | **Default** | **Details** |\n",
-       "| -- | -------- | ----------- | ----------- |\n",
-       "| path | str |  | Directory with stored artifacts. |\n",
-       "| verbose | bool | False |  |\n",
-       "| kwargs |  |  | Additional keyword arguments to be passed to the function<br>`load_from_checkpoint`. |\n",
-       "| **Returns** | **NeuralForecast** |  | **Instantiated `NeuralForecast` class.** |"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show_doc(NeuralForecast.load, title_level=3)"
    ]
@@ -2263,37 +1840,7 @@
    "execution_count": null,
    "id": "c596acd4-c95a-41f3-a710-cb9b2c27459d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.05it/s, v_num=19, train_loss_step=46.40, train_loss_epoch=46.40]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 196.87it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 11.65it/s]\n",
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  2.54it/s, v_num=22, train_loss_step=141.0, train_loss_epoch=141.0]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 224.94it/s]\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[68], line 21\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(input_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     18\u001b[0m output_id_warnings \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     19\u001b[0m     w \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m issued_warnings \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe predictions will have the id as a column\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mmessage)\n\u001b[1;32m     20\u001b[0m ]\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(output_id_warnings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "# id as index warnings\n",
@@ -3889,25 +3436,7 @@
    "execution_count": null,
    "id": "7b980087",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.54it/s, v_num=28, train_loss_step=25.60, train_loss_epoch=25.60]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 115.71it/s]\n",
-      "Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.63it/s, v_num=30, train_loss_step=17.30, train_loss_epoch=17.30]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 189.68it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "# test conformal predictions applied to quantiles if ConformalIntervals.conformalize_quantiles=True\n",

From c0c24ec77f56983cef9b7c2a470f09c134bb064e Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 7 Oct 2024 15:36:03 +0000
Subject: [PATCH 20/25] Missed: Revise type to DFType

---
 nbs/core.ipynb         | 4 ++--
 neuralforecast/core.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 9dc4db61b..253d9e001 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -79,7 +79,7 @@
     "    LocalRobustScaler,\n",
     "    LocalStandardScaler,\n",
     ")\n",
-    "from utilsforecast.compat import DataFrame, Series, pl_DataFrame, pl_Series\n",
+    "from utilsforecast.compat import DataFrame, DFType, Series, pl_DataFrame, pl_Series\n",
     "from utilsforecast.validation import validate_freq\n",
     "\n",
     "from neuralforecast.common._base_model import DistributedConfig\n",
@@ -555,7 +555,7 @@
     "        ):\n",
     "                raise Exception('Set val_size>0 if early stopping is enabled.')\n",
     "        \n",
-    "        self._cs_df: Optional[DataFrame] = None\n",
+    "        self._cs_df: Optional[DFType] = None\n",
     "        self.conformal_intervals: Optional[ConformalIntervals] = None\n",
     "\n",
     "        # Process and save new dataset (in self)\n",
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index c7e73dca9..da0875276 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -24,7 +24,7 @@
     LocalRobustScaler,
     LocalStandardScaler,
 )
-from utilsforecast.compat import DataFrame, Series, pl_DataFrame, pl_Series
+from utilsforecast.compat import DataFrame, DFType, Series, pl_DataFrame, pl_Series
 from utilsforecast.validation import validate_freq
 
 from .common._base_model import DistributedConfig
@@ -487,7 +487,7 @@ def fit(
         ):
             raise Exception("Set val_size>0 if early stopping is enabled.")
 
-        self._cs_df: Optional[DataFrame] = None
+        self._cs_df: Optional[DFType] = None
         self.conformal_intervals: Optional[ConformalIntervals] = None
 
         # Process and save new dataset (in self)

From 079a804035769ad0c19b31b11e361074665a1935 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Mon, 7 Oct 2024 16:31:24 +0000
Subject: [PATCH 21/25] Rename to enable_quantiles; avoid confusing
 interpretation on the meaning of 'conformalize quantiles'

---
 nbs/core.ipynb                                   | 16 ++++++++--------
 nbs/docs/tutorials/20_conformal_prediction.ipynb |  2 +-
 nbs/utils.ipynb                                  |  8 ++++----
 neuralforecast/core.py                           | 13 +++++--------
 neuralforecast/utils.py                          |  8 ++++----
 5 files changed, 22 insertions(+), 25 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 253d9e001..81688d3e7 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -726,12 +726,12 @@
     "\n",
     "        return futr_exog | set(hist_exog)\n",
     "    \n",
-    "    def _get_model_names(self, conformal=False, conformalize_quantiles=False) -> List[str]:\n",
+    "    def _get_model_names(self, conformal=False, enable_quantiles=False) -> List[str]:\n",
     "        names: List[str] = []\n",
     "        count_names = {'model': 0}\n",
     "        for model in self.models:\n",
-    "            if conformal and not conformalize_quantiles and model.loss.outputsize_multiplier > 1:\n",
-    "                # skip conformalize quantile outputs\n",
+    "            if conformal and not enable_quantiles and model.loss.outputsize_multiplier > 1:\n",
+    "                # skip prediction intervals on quantile outputs\n",
     "                continue\n",
     "\n",
     "            model_name = repr(model)\n",
@@ -1024,7 +1024,7 @@
     "                warnings.warn(warn_msg, UserWarning)\n",
     "            else:\n",
     "                level_ = sorted(conformal_level)\n",
-    "                model_names = self._get_model_names(conformal=True, conformalize_quantiles=self.conformal_intervals.conformalize_quantiles)\n",
+    "                model_names = self._get_model_names(conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles)\n",
     "                conformal_method = get_conformal_method(self.conformal_intervals.method)\n",
     "\n",
     "                fcsts_df = conformal_method(\n",
@@ -1680,7 +1680,7 @@
     "        \n",
     "        kept = [time_col, id_col, 'cutoff']\n",
     "        # conformity score for each model\n",
-    "        for model in self._get_model_names(conformal=True, conformalize_quantiles=self.conformal_intervals.conformalize_quantiles):\n",
+    "        for model in self._get_model_names(conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles):\n",
     "            kept.append(model)\n",
     "\n",
     "            # compute absolute error for each model\n",
@@ -3407,7 +3407,7 @@
    "source": [
     "#| hide\n",
     "# test conformal prediction are not applied for models with quantiled-related loss\n",
-    "# by default (ConformalIntervals.conformalize_quantiles=False)\n",
+    "# by default (ConformalIntervals.enable_quantiles=False)\n",
     "\n",
     "conformal_intervals = ConformalIntervals()\n",
     "\n",
@@ -3439,9 +3439,9 @@
    "outputs": [],
    "source": [
     "#| hide\n",
-    "# test conformal predictions applied to quantiles if ConformalIntervals.conformalize_quantiles=True\n",
+    "# test conformal predictions applied to quantiles if ConformalIntervals.enable_quantiles=True\n",
     "\n",
-    "conformal_intervals = ConformalIntervals(conformalize_quantiles=True)\n",
+    "conformal_intervals = ConformalIntervals(enable_quantiles=True)\n",
     "\n",
     "nf = NeuralForecast(models=[NHITS(h=12, input_size=24, max_steps=1, loss=MQLoss(level=[80]))], freq='M')\n",
     "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 345a69b97..9be8dbcb2 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -179,7 +179,7 @@
     "\n",
     "To generate conformal intervals, we specify the desired levels in the `predict` method. Note that in the following example, predictions labeled by `NHITS1` refer to the predictions by model using `DistributionLoss` loss function, which compute the intervals in quantiled manner. Predictions labeled with 'MODEL-conformal-lo/hi-#' are computed by conformal predictions meanwhile those labeled with 'MODEL-lo/hi-#' are computed in quantiled manner. \n",
     "\n",
-    "If we want to conformalize quantiled outputs, we need to set `ConformalIntervals(conformalize_quantiles=True)`."
+    "If we want to conformalize quantiled outputs, we need to set `ConformalIntervals(enable_quantiles=True)`."
    ]
   },
   {
diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index a1994bb80..cd9497e9c 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -579,7 +579,7 @@
     "        self,\n",
     "        n_windows: int = 2,\n",
     "        method: str = \"conformal_distribution\",\n",
-    "        conformalize_quantiles: bool = False,\n",
+    "        enable_quantiles: bool = False,\n",
     "    ):\n",
     "        \"\"\" \n",
     "        n_windows : int\n",
@@ -587,8 +587,8 @@
     "        method : str, default is conformal_distribution\n",
     "            One of the supported methods for the computation of conformal prediction:\n",
     "            conformal_error or conformal_distribution\n",
-    "        conformalize_quantiles : bool, default is False\n",
-    "            If set to True, we shall conformalize quantiled outputs, e.g. prediction made \n",
+    "        enable_quantiles : bool, default is False\n",
+    "            If set to True, we create prediction intervals on top of quantiled outputs, e.g. prediction made \n",
     "            with MQLoss(level=[80]) will be conformalized with the respective conformal\n",
     "            levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').\n",
     "        \"\"\"\n",
@@ -601,7 +601,7 @@
     "            raise ValueError(f\"method must be one of {allowed_methods}\")\n",
     "        self.n_windows = n_windows\n",
     "        self.method = method\n",
-    "        self.conformalize_quantiles = conformalize_quantiles\n",
+    "        self.enable_quantiles = enable_quantiles\n",
     "\n",
     "    def __repr__(self):\n",
     "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')\""
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index da0875276..5d881d208 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -666,18 +666,16 @@ def _get_needed_exog(self):
 
         return futr_exog | set(hist_exog)
 
-    def _get_model_names(
-        self, conformal=False, conformalize_quantiles=False
-    ) -> List[str]:
+    def _get_model_names(self, conformal=False, enable_quantiles=False) -> List[str]:
         names: List[str] = []
         count_names = {"model": 0}
         for model in self.models:
             if (
                 conformal
-                and not conformalize_quantiles
+                and not enable_quantiles
                 and model.loss.outputsize_multiplier > 1
             ):
-                # skip conformalize quantile outputs
+                # skip prediction intervals on quantile outputs
                 continue
 
             model_name = repr(model)
@@ -984,7 +982,7 @@ def predict(
                 level_ = sorted(conformal_level)
                 model_names = self._get_model_names(
                     conformal=True,
-                    conformalize_quantiles=self.conformal_intervals.conformalize_quantiles,
+                    enable_quantiles=self.conformal_intervals.enable_quantiles,
                 )
                 conformal_method = get_conformal_method(self.conformal_intervals.method)
 
@@ -1677,8 +1675,7 @@ def _conformity_scores(
         kept = [time_col, id_col, "cutoff"]
         # conformity score for each model
         for model in self._get_model_names(
-            conformal=True,
-            conformalize_quantiles=self.conformal_intervals.conformalize_quantiles,
+            conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles
         ):
             kept.append(model)
 
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index 4e59670e7..316b506e6 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -458,7 +458,7 @@ def __init__(
         self,
         n_windows: int = 2,
         method: str = "conformal_distribution",
-        conformalize_quantiles: bool = False,
+        enable_quantiles: bool = False,
     ):
         """
         n_windows : int
@@ -466,8 +466,8 @@ def __init__(
         method : str, default is conformal_distribution
             One of the supported methods for the computation of conformal prediction:
             conformal_error or conformal_distribution
-        conformalize_quantiles : bool, default is False
-            If set to True, we shall conformalize quantiled outputs, e.g. prediction made
+        enable_quantiles : bool, default is False
+            If set to True, we create prediction intervals on top of quantiled outputs, e.g. prediction made
             with MQLoss(level=[80]) will be conformalized with the respective conformal
             levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').
         """
@@ -480,7 +480,7 @@ def __init__(
             raise ValueError(f"method must be one of {allowed_methods}")
         self.n_windows = n_windows
         self.method = method
-        self.conformalize_quantiles = conformalize_quantiles
+        self.enable_quantiles = enable_quantiles
 
     def __repr__(self):
         return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')"

From 4561ef1d3c28ecbd234c53dba002c577bb8ab2b2 Mon Sep 17 00:00:00 2001
From: Olivier Sprangers <osprangers@gmail.com>
Date: Tue, 8 Oct 2024 10:42:47 +0200
Subject: [PATCH 22/25] fix_issues

---
 nbs/core.ipynb                                | 167 +++-------
 .../tutorials/20_conformal_prediction.ipynb   | 311 +++++++++++++-----
 nbs/utils.ipynb                               |  29 +-
 neuralforecast/_modidx.py                     |  18 +-
 neuralforecast/core.py                        |  73 ++--
 neuralforecast/utils.py                       |  31 +-
 6 files changed, 350 insertions(+), 279 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 81688d3e7..a0d508c83 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -96,7 +96,7 @@
     "    TimeMixer, KAN, RMoK\n",
     ")\n",
     "from neuralforecast.common._base_auto import BaseAuto, MockTrial\n",
-    "from neuralforecast.utils import ConformalIntervals, get_conformal_method"
+    "from neuralforecast.utils import PredictionIntervals, get_prediction_interval_method"
    ]
   },
   {
@@ -507,7 +507,7 @@
     "        time_col: str = 'ds',\n",
     "        target_col: str = 'y',\n",
     "        distributed_config: Optional[DistributedConfig] = None,\n",
-    "        conformal_intervals: Optional[ConformalIntervals] = None,\n",
+    "        prediction_intervals: Optional[PredictionIntervals] = None,\n",
     "    ) -> None:\n",
     "        \"\"\"Fit the core.NeuralForecast.\n",
     "\n",
@@ -537,7 +537,7 @@
     "            Column that contains the target.\n",
     "        distributed_config : neuralforecast.DistributedConfig\n",
     "            Configuration to use for DDP training. Currently only spark is supported.\n",
-    "        conformal_intervals : ConformalIntervals, optional (default=None)\n",
+    "        prediction_intervals : PredictionIntervals, optional (default=None)\n",
     "            Configuration to calibrate prediction intervals (Conformal Prediction).            \n",
     "\n",
     "        Returns\n",
@@ -556,7 +556,7 @@
     "                raise Exception('Set val_size>0 if early stopping is enabled.')\n",
     "        \n",
     "        self._cs_df: Optional[DFType] = None\n",
-    "        self.conformal_intervals: Optional[ConformalIntervals] = None\n",
+    "        self.prediction_intervals: Optional[PredictionIntervals] = None\n",
     "\n",
     "        # Process and save new dataset (in self)\n",
     "        if isinstance(df, (pd.DataFrame, pl_DataFrame)):\n",
@@ -610,9 +610,8 @@
     "            if self.dataset.min_size < val_size:\n",
     "                warnings.warn('Validation set size is larger than the shorter time-series.')\n",
     "\n",
-    "        if conformal_intervals is not None:\n",
-    "            # conformal prediction\n",
-    "            self.conformal_intervals = conformal_intervals\n",
+    "        if prediction_intervals is not None:\n",
+    "            self.prediction_intervals = prediction_intervals\n",
     "            self._cs_df = self._conformity_scores(\n",
     "                df=df,\n",
     "                id_col=id_col,\n",
@@ -726,12 +725,11 @@
     "\n",
     "        return futr_exog | set(hist_exog)\n",
     "    \n",
-    "    def _get_model_names(self, conformal=False, enable_quantiles=False) -> List[str]:\n",
+    "    def _get_model_names(self, add_level=False) -> List[str]:\n",
     "        names: List[str] = []\n",
     "        count_names = {'model': 0}\n",
     "        for model in self.models:\n",
-    "            if conformal and not enable_quantiles and model.loss.outputsize_multiplier > 1:\n",
-    "                # skip prediction intervals on quantile outputs\n",
+    "            if add_level and model.loss.outputsize_multiplier > 1:\n",
     "                continue\n",
     "\n",
     "            model_name = repr(model)\n",
@@ -856,7 +854,7 @@
     "        sort_df: bool = True,\n",
     "        verbose: bool = False,\n",
     "        engine = None,\n",
-    "        conformal_level: Optional[List[Union[int, float]]] = None,\n",
+    "        level: Optional[List[Union[int, float]]] = None,\n",
     "        **data_kwargs\n",
     "    ):\n",
     "        \"\"\"Predict with core.NeuralForecast.\n",
@@ -878,8 +876,8 @@
     "            Print processing steps.\n",
     "        engine : spark session\n",
     "            Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.\n",
-    "        conformal_level : list of ints or floats, optional (default=None)\n",
-    "            Confidence levels between 0 and 100 for conformal intervals.\n",
+    "        level : list of ints or floats, optional (default=None)\n",
+    "            Confidence levels between 0 and 100.\n",
     "        data_kwargs : kwargs\n",
     "            Extra arguments to be passed to the dataset within each model.\n",
     "\n",
@@ -1015,24 +1013,21 @@
     "            _warn_id_as_idx()\n",
     "            fcsts_df = fcsts_df.set_index(self.id_col)\n",
     "\n",
-    "        # perform conformal predictions\n",
-    "        if conformal_level is not None:\n",
-    "            if self._cs_df is None or self.conformal_intervals is None:\n",
-    "                warn_msg = (\n",
-    "                    'Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores'\n",
-    "                )\n",
-    "                warnings.warn(warn_msg, UserWarning)\n",
+    "        # add prediction intervals\n",
+    "        if level is not None:\n",
+    "            if self._cs_df is None or self.prediction_intervals is None:\n",
+    "                raise Exception('You must fit the model with prediction_intervals to use level.')\n",
     "            else:\n",
-    "                level_ = sorted(conformal_level)\n",
-    "                model_names = self._get_model_names(conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles)\n",
-    "                conformal_method = get_conformal_method(self.conformal_intervals.method)\n",
+    "                level_ = sorted(level)\n",
+    "                model_names = self._get_model_names(add_level=True)\n",
+    "                prediction_interval_method = get_prediction_interval_method(self.prediction_intervals.method)\n",
     "\n",
-    "                fcsts_df = conformal_method(\n",
+    "                fcsts_df = prediction_interval_method(\n",
     "                    fcsts_df,\n",
     "                    self._cs_df,\n",
     "                    model_names=list(model_names),\n",
     "                    level=level_,\n",
-    "                    cs_n_windows=self.conformal_intervals.n_windows,\n",
+    "                    cs_n_windows=self.prediction_intervals.n_windows,\n",
     "                    n_series=len(uids),\n",
     "                    horizon=self.h,\n",
     "                )\n",
@@ -1522,8 +1517,7 @@
     "            \"id_col\": self.id_col,\n",
     "            \"time_col\": self.time_col,\n",
     "            \"target_col\": self.target_col,\n",
-    "            # conformal prediction\n",
-    "            \"conformal_intervals\": self.conformal_intervals,\n",
+    "            \"prediction_intervals\": self.prediction_intervals,\n",
     "            \"_cs_df\": self._cs_df,  # conformity score\n",
     "        }\n",
     "        if save_dataset:\n",
@@ -1613,7 +1607,7 @@
     "        for attr in ['id_col', 'time_col', 'target_col']:\n",
     "            setattr(neuralforecast, attr, config_dict[attr])\n",
     "        # only restore attribute if available\n",
-    "        for attr in ['conformal_intervals', '_cs_df']:\n",
+    "        for attr in ['prediction_intervals', '_cs_df']:\n",
     "            if attr in config_dict.keys():\n",
     "                setattr(neuralforecast, attr, config_dict[attr])\n",
     "\n",
@@ -1647,9 +1641,9 @@
     "        \"\"\"Compute conformity scores.\n",
     "        \n",
     "        We need at least two cross validation errors to compute\n",
-    "        quantiles for prediction intervals (`n_windows=2`, specified by self.conformal_intervals).\n",
+    "        quantiles for prediction intervals (`n_windows=2`, specified by self.prediction_intervals).\n",
     "        \n",
-    "        The exception is raised by the ConformalIntervals data class.\n",
+    "        The exception is raised by the PredictionIntervals data class.\n",
     "\n",
     "        df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,\n",
     "        id_col: str = 'unique_id',\n",
@@ -1657,11 +1651,11 @@
     "        target_col: str = 'y',\n",
     "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
     "        \"\"\"\n",
-    "        if self.conformal_intervals is None:\n",
-    "            raise AttributeError('Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores')\n",
+    "        if self.prediction_intervals is None:\n",
+    "            raise AttributeError('Please rerun the `fit` method passing a valid prediction_interval setting to compute conformity scores')\n",
     "                    \n",
     "        min_size = ufp.counts_by_id(df, id_col)['counts'].min()\n",
-    "        min_samples = self.h * self.conformal_intervals.n_windows + 1\n",
+    "        min_samples = self.h * self.prediction_intervals.n_windows + 1\n",
     "        if min_size < min_samples:\n",
     "            raise ValueError(\n",
     "                \"Minimum required samples in each serie for the prediction intervals \"\n",
@@ -1672,7 +1666,7 @@
     "        cv_results = self.cross_validation(\n",
     "            df=df,\n",
     "            static_df=static_df,\n",
-    "            n_windows=self.conformal_intervals.n_windows,\n",
+    "            n_windows=self.prediction_intervals.n_windows,\n",
     "            id_col=id_col,\n",
     "            time_col=time_col,\n",
     "            target_col=target_col,\n",
@@ -1680,7 +1674,7 @@
     "        \n",
     "        kept = [time_col, id_col, 'cutoff']\n",
     "        # conformity score for each model\n",
-    "        for model in self._get_model_names(conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles):\n",
+    "        for model in self._get_model_names(add_level=True):\n",
     "            kept.append(model)\n",
     "\n",
     "            # compute absolute error for each model\n",
@@ -2622,9 +2616,9 @@
     "    ],\n",
     "    freq='M'\n",
     ")\n",
-    "conformal_intervals = ConformalIntervals()\n",
-    "fcst.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
-    "forecasts1 = fcst.predict(futr_df=AirPassengersPanel_test, conformal_level=[50])\n",
+    "prediction_intervals = PredictionIntervals()\n",
+    "fcst.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)\n",
+    "forecasts1 = fcst.predict(futr_df=AirPassengersPanel_test, level=[50])\n",
     "save_paths = ['./examples/debug_run/']\n",
     "try:\n",
     "    s3fs.S3FileSystem().ls('s3://nixtla-tmp')    \n",
@@ -2638,7 +2632,7 @@
     "for path in save_paths:\n",
     "    fcst.save(path=path, model_index=None, overwrite=True, save_dataset=True)\n",
     "    fcst2 = NeuralForecast.load(path=path)\n",
-    "    forecasts2 = fcst2.predict(futr_df=AirPassengersPanel_test, conformal_level=[50])\n",
+    "    forecasts2 = fcst2.predict(futr_df=AirPassengersPanel_test, level=[50])\n",
     "    pd.testing.assert_frame_equal(forecasts1, forecasts2[forecasts1.columns])"
    ]
   },
@@ -3332,19 +3326,19 @@
     "#| hide\n",
     "# test conformal prediction, method=conformal_distribution\n",
     "\n",
-    "conformal_intervals = ConformalIntervals()\n",
+    "prediction_intervals = PredictionIntervals()\n",
     "\n",
     "models = []\n",
-    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "for nf_model in [NHITS, RNN, TSMixer]:\n",
     "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
-    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "    if nf_model.__name__ == \"TSMixer\":\n",
     "        params.update({\"n_series\": 2})\n",
     "    models.append(nf_model(**params))\n",
     "\n",
     "\n",
     "nf = NeuralForecast(models=models, freq='M')\n",
-    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
-    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
+    "nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, level=[90])"
    ]
   },
   {
@@ -3358,19 +3352,19 @@
     "#| polars\n",
     "# test conformal prediction works for polar dataframe\n",
     "\n",
-    "conformal_intervals = ConformalIntervals()\n",
+    "prediction_intervals = PredictionIntervals()\n",
     "\n",
     "models = []\n",
-    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "for nf_model in [NHITS, RNN, TSMixer]:\n",
     "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
-    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "    if nf_model.__name__ == \"TSMixer\":\n",
     "        params.update({\"n_series\": 2})\n",
     "    models.append(nf_model(**params))\n",
     "\n",
     "\n",
     "nf = NeuralForecast(models=models, freq='1mo')\n",
-    "nf.fit(AirPassengers_pl, conformal_intervals=conformal_intervals, time_col='time', id_col='uid', target_col='target')\n",
-    "preds = nf.predict(conformal_level=[10, 50, 90])"
+    "nf.fit(AirPassengers_pl, prediction_intervals=prediction_intervals, time_col='time', id_col='uid', target_col='target')\n",
+    "preds = nf.predict(level=[90])"
    ]
   },
   {
@@ -3383,84 +3377,19 @@
     "#| hide\n",
     "# test conformal prediction, method=conformal_error\n",
     "\n",
-    "conformal_intervals = ConformalIntervals(method=\"conformal_error\")\n",
+    "prediction_intervals = PredictionIntervals(method=\"conformal_error\")\n",
     "\n",
     "models = []\n",
-    "for nf_model in [NHITS, RNN, StemGNN]:\n",
+    "for nf_model in [NHITS, RNN, TSMixer]:\n",
     "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
-    "    if nf_model.__name__ == \"StemGNN\":\n",
+    "    if nf_model.__name__ == \"TSMixer\":\n",
     "        params.update({\"n_series\": 2})\n",
     "    models.append(nf_model(**params))\n",
     "\n",
     "\n",
     "nf = NeuralForecast(models=models, freq='M')\n",
-    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
-    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d25b2cd2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "# test conformal prediction are not applied for models with quantiled-related loss\n",
-    "# by default (ConformalIntervals.enable_quantiles=False)\n",
-    "\n",
-    "conformal_intervals = ConformalIntervals()\n",
-    "\n",
-    "models = []\n",
-    "for nf_model in [NHITS, RNN]:\n",
-    "    params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n",
-    "    if nf_model.__name__ == \"NHITS\":\n",
-    "        params.update({\"loss\": MQLoss(level=[80])})\n",
-    "    models.append(nf_model(**params))\n",
-    "\n",
-    "\n",
-    "nf = NeuralForecast(models=models, freq='M')\n",
-    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
-    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])\n",
-    "\n",
-    "pred_cols = [\n",
-    "    'NHITS-median', 'NHITS-lo-80', 'NHITS-hi-80', 'RNN',\n",
-    "    'RNN-conformal-lo-90', 'RNN-conformal-lo-50', 'RNN-conformal-lo-10',\n",
-    "    'RNN-conformal-hi-10', 'RNN-conformal-hi-50', 'RNN-conformal-hi-90'\n",
-    "]\n",
-    "assert all([col in preds.columns for col in pred_cols])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b980087",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "# test conformal predictions applied to quantiles if ConformalIntervals.enable_quantiles=True\n",
-    "\n",
-    "conformal_intervals = ConformalIntervals(enable_quantiles=True)\n",
-    "\n",
-    "nf = NeuralForecast(models=[NHITS(h=12, input_size=24, max_steps=1, loss=MQLoss(level=[80]))], freq='M')\n",
-    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)\n",
-    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])\n",
-    "\n",
-    "pred_cols = [\n",
-    "       'NHITS-median', 'NHITS-lo-80', 'NHITS-hi-80',\n",
-    "       'NHITS-median-conformal-lo-90', 'NHITS-median-conformal-lo-50',\n",
-    "       'NHITS-median-conformal-lo-10', 'NHITS-median-conformal-hi-10',\n",
-    "       'NHITS-median-conformal-hi-50', 'NHITS-median-conformal-hi-90',\n",
-    "       'NHITS-lo-80-conformal-lo-90', 'NHITS-lo-80-conformal-lo-50',\n",
-    "       'NHITS-lo-80-conformal-lo-10', 'NHITS-lo-80-conformal-hi-10',\n",
-    "       'NHITS-lo-80-conformal-hi-50', 'NHITS-lo-80-conformal-hi-90',\n",
-    "       'NHITS-hi-80-conformal-lo-90', 'NHITS-hi-80-conformal-lo-50',\n",
-    "       'NHITS-hi-80-conformal-lo-10', 'NHITS-hi-80-conformal-hi-10',\n",
-    "       'NHITS-hi-80-conformal-hi-50', 'NHITS-hi-80-conformal-hi-90'\n",
-    "]\n",
-    "\n",
-    "assert all([col in preds.columns for col in pred_cols])\n"
+    "nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)\n",
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, level=[90])"
    ]
   }
  ],
diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index 9be8dbcb2..e6b5c883d 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Conformal Prediction\n",
+    "# Uncertainty quantification with Conformal Prediction\n",
     "> Tutorial on how to train neuralforecast models and obtain prediction intervals using the conformal prediction methods"
    ]
   },
@@ -38,10 +38,9 @@
     "import matplotlib.pyplot as plt\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import NHITS\n",
-    "from neuralforecast.models import NLinear\n",
     "from neuralforecast.utils import AirPassengersPanel\n",
-    "from neuralforecast.utils import ConformalIntervals\n",
-    "from neuralforecast.losses.pytorch import DistributionLoss\n"
+    "from neuralforecast.utils import PredictionIntervals\n",
+    "from neuralforecast.losses.pytorch import DistributionLoss, MAE"
    ]
   },
   {
@@ -60,7 +59,7 @@
    "source": [
     "## Data\n",
     "\n",
-    "We simply use the AirPassengers dataset for the demonstration of conformal prediction.\n"
+    "We use the AirPassengers dataset for the demonstration of conformal prediction.\n"
    ]
   },
   {
@@ -81,11 +80,14 @@
    "source": [
     "## Model training\n",
     "\n",
-    "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `ConformalIntervals` class and pass this to the `fit` method. By default, `ConformalIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. We also train a MLP model using DistributionLoss to demonstate the difference between conformal prediction and quantiled outputs. \n",
+    "We now train a NHITS model on the above dataset. To support conformal predictions, we must first instantiate the `PredictionIntervals` class and pass this to the `fit` method. By default, `PredictionIntervals` class employs `n_windows=2` for the corss-validation during the computation of conformity scores. We also train a MLP model using DistributionLoss to demonstate the difference between conformal prediction and quantiled outputs. \n",
     "\n",
-    "<br>\n",
+    "By default, `PredictionIntervals` class employs `method=conformal_distribution` for the conformal predictions, but it also supports `method=conformal_error`. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` method calculates quantiles directly from errors.\n",
     "\n",
-    "By default, `ConformalIntervals` class employs method=conformal_distribution for the conformal predictions. `method=conformal_error` is also supported. The `conformal_distribution` method calculates forecast paths using the absolute errors and based on them calculates quantiles. The `conformal_error` calculates quantiles directly from errors.\n"
+    "We consider two models below:\n",
+    "\n",
+    "1. A model trained using a point loss function (`MAE`), where we quantify the uncertainty using conformal prediction. This case is labeled with `NHITS`.\n",
+    "2. A model trained using a `DistributionLoss('Normal')`, where we quantify the uncertainty by training the model to fit the parameters of a Normal distribution. This case is labeled with `NHITS1`.\n"
    ]
   },
   {
@@ -102,73 +104,212 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                             "
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e95f40e90284ad88509b20e11bde568",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4ddd3ee5efe47e69eb3357af61c51fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.48it/s, v_num=31, train_loss_step=14.40, train_loss_epoch=14.40]"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25c3c9aadfe54f41ac608c01284a3a4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f39126dacfbe4081835ae886d7c4efc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 204.83it/s]\n",
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.30it/s, v_num=33, train_loss_step=5.830, train_loss_epoch=5.830]   \n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 55.94it/s]\n",
-      "                                                                             "
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e65e255619774a4db3945b7b41d96af7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e830c80706994e1ab41c846ce8aa437d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s, v_num=35, train_loss_step=10.30, train_loss_epoch=10.30]\n",
-      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.15it/s, v_num=36, train_loss_step=5.640, train_loss_epoch=5.640]   \n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f7418b413e44e42a1eb4c0dab8d321e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc5a3118e2b44a91abd23f92ac4f3bfd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c029b2370e494a7a93f99989ec4a5304",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2a862454b834645a454e2134d12a715",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "036eaffe339645f8a7723cefede335f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "655e0ef54144434798b2b7b40122e6b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cda10db85e0940cbad25cc92f84debc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7af7c58c97e94236bbc136cd6b451c45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "horizon = 12\n",
     "input_size = 24\n",
     "\n",
-    "conformal_intervals = ConformalIntervals()\n",
+    "prediction_intervals = PredictionIntervals()\n",
     "\n",
-    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100), NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[10, 50, 90]))]\n",
+    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100, loss=MAE()), \n",
+    "          NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[90]))]\n",
     "nf = NeuralForecast(models=models, freq='ME')\n",
-    "nf.fit(AirPassengersPanel_train, conformal_intervals=conformal_intervals)"
+    "nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)"
    ]
   },
   {
@@ -177,9 +318,7 @@
    "source": [
     "## Forecasting\n",
     "\n",
-    "To generate conformal intervals, we specify the desired levels in the `predict` method. Note that in the following example, predictions labeled by `NHITS1` refer to the predictions by model using `DistributionLoss` loss function, which compute the intervals in quantiled manner. Predictions labeled with 'MODEL-conformal-lo/hi-#' are computed by conformal predictions meanwhile those labeled with 'MODEL-lo/hi-#' are computed in quantiled manner. \n",
-    "\n",
-    "If we want to conformalize quantiled outputs, we need to set `ConformalIntervals(enable_quantiles=True)`."
+    "To generate conformal intervals, we specify the desired levels in the `predict` method. "
    ]
   },
   {
@@ -188,23 +327,36 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50872a892eff4a8aa112e895186e9813",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 173.71it/s]\n",
-      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 161.85it/s]\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09d733fc4dd74a1ea4232df550511bd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "preds = nf.predict(futr_df=AirPassengersPanel_test, conformal_level=[10, 50, 90])"
+    "preds = nf.predict(futr_df=AirPassengersPanel_test, level=[90])"
    ]
   },
   {
@@ -214,7 +366,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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9qMTERPXo0UNDhw7VihUrqs1cAAAAQE3Cx44BAAAAQC20Z88excXFSZIWLFggV1fXch87b948HTp0SNnZ2Q9pdQAAAIB9o/kCAAAAALVQRkaGMjIyJElz5sypUPNl586dWr9+/cNaGgAAAGD3+NgxAAAAAKjmbPmxYwAAAAAePp58AQAAAIBqjr+ZAwAAAOxLnUe9AAAAAAAAAAAAgJqE5gsAAAAAAAAAAIAN0XwBAAAAAAAAAACwIZovAAAAAAAAAAAANkTzBQAAAAAAAAAAwIZovgAAAAAAAAAAANgQzRcAAAAAAAAAAAAbovkCAAAAAAAAAABgQ/8HqpfpJB3WTg0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 2000x700 with 2 Axes>"
       ]
@@ -225,16 +377,27 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(2, 1, figsize = (20, 7))\n",
-    "plot_df = pd.concat([AirPassengersPanel_train, preds]).set_index('ds')\n",
+    "plot_df = pd.concat([AirPassengersPanel_train, preds])\n",
     "\n",
     "plot_df = plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).iloc[-50:]\n",
-    "plot_df.drop([x for x in plot_df.columns if 'NHITS1' in x], axis=1).plot(ax=ax1, linewidth=2)\n",
-    "plot_df.drop([x for x in plot_df.columns if 'NHITS-' in x or x == \"NHITS\"], axis=1).plot(ax=ax2, linewidth=2)\n",
     "\n",
+    "ax1.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
+    "ax1.plot(plot_df['ds'], plot_df['NHITS1'], c='blue', label='median')\n",
+    "ax1.fill_between(x=plot_df['ds'][-12:], \n",
+    "                 y1=plot_df['NHITS1-lo-90'][-12:].values,\n",
+    "                 y2=plot_df['NHITS1-hi-90'][-12:].values,\n",
+    "                 alpha=0.4, label='level 90')\n",
     "ax1.set_title('AirPassengers Forecast', fontsize=18)\n",
     "ax1.set_ylabel('Monthly Passengers', fontsize=15)\n",
     "ax1.legend(prop={'size': 10})\n",
     "ax1.grid()\n",
+    "\n",
+    "ax2.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
+    "ax2.plot(plot_df['ds'], plot_df['NHITS'], c='blue', label='median')\n",
+    "ax2.fill_between(x=plot_df['ds'][-12:], \n",
+    "                 y1=plot_df['NHITS-lo-90'][-12:].values,\n",
+    "                 y2=plot_df['NHITS-hi-90'][-12:].values,\n",
+    "                 alpha=0.4, label='level 90')\n",
     "ax2.set_ylabel('Monthly Passengers', fontsize=15)\n",
     "ax2.set_xlabel('Timestamp [t]', fontsize=15)\n",
     "ax2.legend(prop={'size': 10})\n",
diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
index cd9497e9c..e202c9adc 100644
--- a/nbs/utils.ipynb
+++ b/nbs/utils.ipynb
@@ -561,7 +561,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 5. Conformal Prediction"
+    "# 5. Prediction Intervals"
    ]
   },
   {
@@ -572,25 +572,20 @@
    "source": [
     "#| export\n",
     "\n",
-    "class ConformalIntervals:\n",
-    "    \"\"\"Class for storing conformal intervals metadata information.\"\"\"\n",
+    "class PredictionIntervals:\n",
+    "    \"\"\"Class for storing prediction intervals metadata information.\"\"\"\n",
     "\n",
     "    def __init__(\n",
     "        self,\n",
     "        n_windows: int = 2,\n",
     "        method: str = \"conformal_distribution\",\n",
-    "        enable_quantiles: bool = False,\n",
     "    ):\n",
     "        \"\"\" \n",
     "        n_windows : int\n",
     "            Number of windows to evaluate.\n",
     "        method : str, default is conformal_distribution\n",
-    "            One of the supported methods for the computation of conformal prediction:\n",
+    "            One of the supported methods for the computation of prediction intervals:\n",
     "            conformal_error or conformal_distribution\n",
-    "        enable_quantiles : bool, default is False\n",
-    "            If set to True, we create prediction intervals on top of quantiled outputs, e.g. prediction made \n",
-    "            with MQLoss(level=[80]) will be conformalized with the respective conformal\n",
-    "            levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').\n",
     "        \"\"\"\n",
     "        if n_windows < 2:\n",
     "            raise ValueError(\n",
@@ -601,10 +596,9 @@
     "            raise ValueError(f\"method must be one of {allowed_methods}\")\n",
     "        self.n_windows = n_windows\n",
     "        self.method = method\n",
-    "        self.enable_quantiles = enable_quantiles\n",
     "\n",
     "    def __repr__(self):\n",
-    "        return f\"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')\""
+    "        return f\"PredictionIntervals(n_windows={self.n_windows}, method='{self.method}')\""
    ]
   },
   {
@@ -614,7 +608,6 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "\n",
     "def add_conformal_distribution_intervals(\n",
     "    fcst_df: DFType, \n",
     "    cs_df: DFType,\n",
@@ -645,8 +638,8 @@
     "            axis=0,\n",
     "        )\n",
     "        quantiles = quantiles.reshape(len(cuts), -1).T\n",
-    "        lo_cols = [f\"{model}-conformal-lo-{lv}\" for lv in reversed(level)]\n",
-    "        hi_cols = [f\"{model}-conformal-hi-{lv}\" for lv in level]\n",
+    "        lo_cols = [f\"{model}-lo-{lv}\" for lv in reversed(level)]\n",
+    "        hi_cols = [f\"{model}-hi-{lv}\" for lv in level]\n",
     "        out_cols = lo_cols + hi_cols\n",
     "        fcst_df = ufp.assign_columns(fcst_df, out_cols, quantiles)\n",
     "    return fcst_df"
@@ -659,7 +652,6 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "\n",
     "def add_conformal_error_intervals(\n",
     "    fcst_df: DFType, \n",
     "    cs_df: DFType, \n",
@@ -687,8 +679,8 @@
     "            axis=0,\n",
     "        )\n",
     "        quantiles = quantiles.reshape(len(cuts), -1)\n",
-    "        lo_cols = [f\"{model}-conformal-lo-{lv}\" for lv in reversed(level)]\n",
-    "        hi_cols = [f\"{model}-conformal-hi-{lv}\" for lv in level]\n",
+    "        lo_cols = [f\"{model}-lo-{lv}\" for lv in reversed(level)]\n",
+    "        hi_cols = [f\"{model}-hi-{lv}\" for lv in level]\n",
     "        quantiles = np.vstack([mean - quantiles[::-1], mean + quantiles]).T\n",
     "        columns = lo_cols + hi_cols\n",
     "        fcst_df = ufp.assign_columns(fcst_df, columns, quantiles)\n",
@@ -702,8 +694,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "\n",
-    "def get_conformal_method(method: str):\n",
+    "def get_prediction_interval_method(method: str):\n",
     "    available_methods = {\n",
     "        \"conformal_distribution\": add_conformal_distribution_intervals,\n",
     "        \"conformal_error\": add_conformal_error_intervals,\n",
diff --git a/neuralforecast/_modidx.py b/neuralforecast/_modidx.py
index 41ae2929f..a49b3529e 100644
--- a/neuralforecast/_modidx.py
+++ b/neuralforecast/_modidx.py
@@ -1444,13 +1444,7 @@
                                                                                       'neuralforecast/tsdataset.py'),
                                           'neuralforecast.tsdataset._FilesDataset.__init__': ( 'tsdataset.html#_filesdataset.__init__',
                                                                                                'neuralforecast/tsdataset.py')},
-            'neuralforecast.utils': { 'neuralforecast.utils.ConformalIntervals': ( 'utils.html#conformalintervals',
-                                                                                   'neuralforecast/utils.py'),
-                                      'neuralforecast.utils.ConformalIntervals.__init__': ( 'utils.html#conformalintervals.__init__',
-                                                                                            'neuralforecast/utils.py'),
-                                      'neuralforecast.utils.ConformalIntervals.__repr__': ( 'utils.html#conformalintervals.__repr__',
-                                                                                            'neuralforecast/utils.py'),
-                                      'neuralforecast.utils.DayOfMonth': ('utils.html#dayofmonth', 'neuralforecast/utils.py'),
+            'neuralforecast.utils': { 'neuralforecast.utils.DayOfMonth': ('utils.html#dayofmonth', 'neuralforecast/utils.py'),
                                       'neuralforecast.utils.DayOfMonth.__call__': ( 'utils.html#dayofmonth.__call__',
                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.DayOfWeek': ('utils.html#dayofweek', 'neuralforecast/utils.py'),
@@ -1468,6 +1462,12 @@
                                       'neuralforecast.utils.MonthOfYear': ('utils.html#monthofyear', 'neuralforecast/utils.py'),
                                       'neuralforecast.utils.MonthOfYear.__call__': ( 'utils.html#monthofyear.__call__',
                                                                                      'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.PredictionIntervals': ( 'utils.html#predictionintervals',
+                                                                                    'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.PredictionIntervals.__init__': ( 'utils.html#predictionintervals.__init__',
+                                                                                             'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.PredictionIntervals.__repr__': ( 'utils.html#predictionintervals.__repr__',
+                                                                                             'neuralforecast/utils.py'),
                                       'neuralforecast.utils.SecondOfMinute': ('utils.html#secondofminute', 'neuralforecast/utils.py'),
                                       'neuralforecast.utils.SecondOfMinute.__call__': ( 'utils.html#secondofminute.__call__',
                                                                                         'neuralforecast/utils.py'),
@@ -1488,9 +1488,9 @@
                                       'neuralforecast.utils.augment_calendar_df': ( 'utils.html#augment_calendar_df',
                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.generate_series': ('utils.html#generate_series', 'neuralforecast/utils.py'),
-                                      'neuralforecast.utils.get_conformal_method': ( 'utils.html#get_conformal_method',
-                                                                                     'neuralforecast/utils.py'),
                                       'neuralforecast.utils.get_indexer_raise_missing': ( 'utils.html#get_indexer_raise_missing',
                                                                                           'neuralforecast/utils.py'),
+                                      'neuralforecast.utils.get_prediction_interval_method': ( 'utils.html#get_prediction_interval_method',
+                                                                                               'neuralforecast/utils.py'),
                                       'neuralforecast.utils.time_features_from_frequency_str': ( 'utils.html#time_features_from_frequency_str',
                                                                                                  'neuralforecast/utils.py')}}}
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index 5d881d208..180a8a004 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -69,7 +69,7 @@
     RMoK,
 )
 from .common._base_auto import BaseAuto, MockTrial
-from .utils import ConformalIntervals, get_conformal_method
+from .utils import PredictionIntervals, get_prediction_interval_method
 
 # %% ../nbs/core.ipynb 5
 # this disables warnings about the number of workers in the dataloaders
@@ -439,7 +439,7 @@ def fit(
         time_col: str = "ds",
         target_col: str = "y",
         distributed_config: Optional[DistributedConfig] = None,
-        conformal_intervals: Optional[ConformalIntervals] = None,
+        prediction_intervals: Optional[PredictionIntervals] = None,
     ) -> None:
         """Fit the core.NeuralForecast.
 
@@ -469,7 +469,7 @@ def fit(
             Column that contains the target.
         distributed_config : neuralforecast.DistributedConfig
             Configuration to use for DDP training. Currently only spark is supported.
-        conformal_intervals : ConformalIntervals, optional (default=None)
+        prediction_intervals : PredictionIntervals, optional (default=None)
             Configuration to calibrate prediction intervals (Conformal Prediction).
 
         Returns
@@ -488,7 +488,7 @@ def fit(
             raise Exception("Set val_size>0 if early stopping is enabled.")
 
         self._cs_df: Optional[DFType] = None
-        self.conformal_intervals: Optional[ConformalIntervals] = None
+        self.prediction_intervals: Optional[PredictionIntervals] = None
 
         # Process and save new dataset (in self)
         if isinstance(df, (pd.DataFrame, pl_DataFrame)):
@@ -546,9 +546,8 @@ def fit(
                     "Validation set size is larger than the shorter time-series."
                 )
 
-        if conformal_intervals is not None:
-            # conformal prediction
-            self.conformal_intervals = conformal_intervals
+        if prediction_intervals is not None:
+            self.prediction_intervals = prediction_intervals
             self._cs_df = self._conformity_scores(
                 df=df,
                 id_col=id_col,
@@ -666,16 +665,11 @@ def _get_needed_exog(self):
 
         return futr_exog | set(hist_exog)
 
-    def _get_model_names(self, conformal=False, enable_quantiles=False) -> List[str]:
+    def _get_model_names(self, add_level=False) -> List[str]:
         names: List[str] = []
         count_names = {"model": 0}
         for model in self.models:
-            if (
-                conformal
-                and not enable_quantiles
-                and model.loss.outputsize_multiplier > 1
-            ):
-                # skip prediction intervals on quantile outputs
+            if add_level and model.loss.outputsize_multiplier > 1:
                 continue
 
             model_name = repr(model)
@@ -808,7 +802,7 @@ def predict(
         sort_df: bool = True,
         verbose: bool = False,
         engine=None,
-        conformal_level: Optional[List[Union[int, float]]] = None,
+        level: Optional[List[Union[int, float]]] = None,
         **data_kwargs,
     ):
         """Predict with core.NeuralForecast.
@@ -830,8 +824,8 @@ def predict(
             Print processing steps.
         engine : spark session
             Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.
-        conformal_level : list of ints or floats, optional (default=None)
-            Confidence levels between 0 and 100 for conformal intervals.
+        level : list of ints or floats, optional (default=None)
+            Confidence levels between 0 and 100.
         data_kwargs : kwargs
             Extra arguments to be passed to the dataset within each model.
 
@@ -973,25 +967,25 @@ def predict(
             _warn_id_as_idx()
             fcsts_df = fcsts_df.set_index(self.id_col)
 
-        # perform conformal predictions
-        if conformal_level is not None:
-            if self._cs_df is None or self.conformal_intervals is None:
-                warn_msg = "Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores"
-                warnings.warn(warn_msg, UserWarning)
+        # add prediction intervals
+        if level is not None:
+            if self._cs_df is None or self.prediction_intervals is None:
+                raise Exception(
+                    "You must fit the model with prediction_intervals to use level."
+                )
             else:
-                level_ = sorted(conformal_level)
-                model_names = self._get_model_names(
-                    conformal=True,
-                    enable_quantiles=self.conformal_intervals.enable_quantiles,
+                level_ = sorted(level)
+                model_names = self._get_model_names(add_level=True)
+                prediction_interval_method = get_prediction_interval_method(
+                    self.prediction_intervals.method
                 )
-                conformal_method = get_conformal_method(self.conformal_intervals.method)
 
-                fcsts_df = conformal_method(
+                fcsts_df = prediction_interval_method(
                     fcsts_df,
                     self._cs_df,
                     model_names=list(model_names),
                     level=level_,
-                    cs_n_windows=self.conformal_intervals.n_windows,
+                    cs_n_windows=self.prediction_intervals.n_windows,
                     n_series=len(uids),
                     horizon=self.h,
                 )
@@ -1505,8 +1499,7 @@ def save(
             "id_col": self.id_col,
             "time_col": self.time_col,
             "target_col": self.target_col,
-            # conformal prediction
-            "conformal_intervals": self.conformal_intervals,
+            "prediction_intervals": self.prediction_intervals,
             "_cs_df": self._cs_df,  # conformity score
         }
         if save_dataset:
@@ -1605,7 +1598,7 @@ def load(path, verbose=False, **kwargs):
         for attr in ["id_col", "time_col", "target_col"]:
             setattr(neuralforecast, attr, config_dict[attr])
         # only restore attribute if available
-        for attr in ["conformal_intervals", "_cs_df"]:
+        for attr in ["prediction_intervals", "_cs_df"]:
             if attr in config_dict.keys():
                 setattr(neuralforecast, attr, config_dict[attr])
 
@@ -1639,9 +1632,9 @@ def _conformity_scores(
         """Compute conformity scores.
 
         We need at least two cross validation errors to compute
-        quantiles for prediction intervals (`n_windows=2`, specified by self.conformal_intervals).
+        quantiles for prediction intervals (`n_windows=2`, specified by self.prediction_intervals).
 
-        The exception is raised by the ConformalIntervals data class.
+        The exception is raised by the PredictionIntervals data class.
 
         df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,
         id_col: str = 'unique_id',
@@ -1649,13 +1642,13 @@ def _conformity_scores(
         target_col: str = 'y',
         static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
         """
-        if self.conformal_intervals is None:
+        if self.prediction_intervals is None:
             raise AttributeError(
-                "Please rerun the `fit` method passing a valid conformal_interval settings to compute conformity scores"
+                "Please rerun the `fit` method passing a valid prediction_interval setting to compute conformity scores"
             )
 
         min_size = ufp.counts_by_id(df, id_col)["counts"].min()
-        min_samples = self.h * self.conformal_intervals.n_windows + 1
+        min_samples = self.h * self.prediction_intervals.n_windows + 1
         if min_size < min_samples:
             raise ValueError(
                 "Minimum required samples in each serie for the prediction intervals "
@@ -1666,7 +1659,7 @@ def _conformity_scores(
         cv_results = self.cross_validation(
             df=df,
             static_df=static_df,
-            n_windows=self.conformal_intervals.n_windows,
+            n_windows=self.prediction_intervals.n_windows,
             id_col=id_col,
             time_col=time_col,
             target_col=target_col,
@@ -1674,9 +1667,7 @@ def _conformity_scores(
 
         kept = [time_col, id_col, "cutoff"]
         # conformity score for each model
-        for model in self._get_model_names(
-            conformal=True, enable_quantiles=self.conformal_intervals.enable_quantiles
-        ):
+        for model in self._get_model_names(add_level=True):
             kept.append(model)
 
             # compute absolute error for each model
diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
index 316b506e6..9d15ce1ca 100644
--- a/neuralforecast/utils.py
+++ b/neuralforecast/utils.py
@@ -4,8 +4,9 @@
 __all__ = ['AirPassengers', 'AirPassengersDF', 'unique_id', 'ds', 'y', 'AirPassengersPanel', 'snaive', 'airline1_dummy',
            'airline2_dummy', 'AirPassengersStatic', 'generate_series', 'TimeFeature', 'SecondOfMinute', 'MinuteOfHour',
            'HourOfDay', 'DayOfWeek', 'DayOfMonth', 'DayOfYear', 'MonthOfYear', 'WeekOfYear',
-           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing', 'ConformalIntervals',
-           'add_conformal_distribution_intervals', 'add_conformal_error_intervals', 'get_conformal_method']
+           'time_features_from_frequency_str', 'augment_calendar_df', 'get_indexer_raise_missing',
+           'PredictionIntervals', 'add_conformal_distribution_intervals', 'add_conformal_error_intervals',
+           'get_prediction_interval_method']
 
 # %% ../nbs/utils.ipynb 3
 import random
@@ -451,25 +452,20 @@ def get_indexer_raise_missing(idx: pd.Index, vals: List[str]) -> List[int]:
     return idxs
 
 # %% ../nbs/utils.ipynb 31
-class ConformalIntervals:
-    """Class for storing conformal intervals metadata information."""
+class PredictionIntervals:
+    """Class for storing prediction intervals metadata information."""
 
     def __init__(
         self,
         n_windows: int = 2,
         method: str = "conformal_distribution",
-        enable_quantiles: bool = False,
     ):
         """
         n_windows : int
             Number of windows to evaluate.
         method : str, default is conformal_distribution
-            One of the supported methods for the computation of conformal prediction:
+            One of the supported methods for the computation of prediction intervals:
             conformal_error or conformal_distribution
-        enable_quantiles : bool, default is False
-            If set to True, we create prediction intervals on top of quantiled outputs, e.g. prediction made
-            with MQLoss(level=[80]) will be conformalized with the respective conformal
-            levels (prediction columns having 'model-lo/hi-80-conformal-lo/hi-#').
         """
         if n_windows < 2:
             raise ValueError(
@@ -480,10 +476,11 @@ def __init__(
             raise ValueError(f"method must be one of {allowed_methods}")
         self.n_windows = n_windows
         self.method = method
-        self.enable_quantiles = enable_quantiles
 
     def __repr__(self):
-        return f"ConformalIntervals(n_windows={self.n_windows}, method='{self.method}')"
+        return (
+            f"PredictionIntervals(n_windows={self.n_windows}, method='{self.method}')"
+        )
 
 # %% ../nbs/utils.ipynb 32
 def add_conformal_distribution_intervals(
@@ -516,8 +513,8 @@ def add_conformal_distribution_intervals(
             axis=0,
         )
         quantiles = quantiles.reshape(len(cuts), -1).T
-        lo_cols = [f"{model}-conformal-lo-{lv}" for lv in reversed(level)]
-        hi_cols = [f"{model}-conformal-hi-{lv}" for lv in level]
+        lo_cols = [f"{model}-lo-{lv}" for lv in reversed(level)]
+        hi_cols = [f"{model}-hi-{lv}" for lv in level]
         out_cols = lo_cols + hi_cols
         fcst_df = ufp.assign_columns(fcst_df, out_cols, quantiles)
     return fcst_df
@@ -550,15 +547,15 @@ def add_conformal_error_intervals(
             axis=0,
         )
         quantiles = quantiles.reshape(len(cuts), -1)
-        lo_cols = [f"{model}-conformal-lo-{lv}" for lv in reversed(level)]
-        hi_cols = [f"{model}-conformal-hi-{lv}" for lv in level]
+        lo_cols = [f"{model}-lo-{lv}" for lv in reversed(level)]
+        hi_cols = [f"{model}-hi-{lv}" for lv in level]
         quantiles = np.vstack([mean - quantiles[::-1], mean + quantiles]).T
         columns = lo_cols + hi_cols
         fcst_df = ufp.assign_columns(fcst_df, columns, quantiles)
     return fcst_df
 
 # %% ../nbs/utils.ipynb 34
-def get_conformal_method(method: str):
+def get_prediction_interval_method(method: str):
     available_methods = {
         "conformal_distribution": add_conformal_distribution_intervals,
         "conformal_error": add_conformal_error_intervals,

From f23661a35e97f0116dbd28f57023710989689dd2 Mon Sep 17 00:00:00 2001
From: Olivier Sprangers <osprangers@gmail.com>
Date: Tue, 8 Oct 2024 13:22:18 +0200
Subject: [PATCH 23/25] fix_example

---
 .../tutorials/20_conformal_prediction.ipynb   | 54 ++++++++++---------
 1 file changed, 28 insertions(+), 26 deletions(-)

diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
index e6b5c883d..d781c668e 100644
--- a/nbs/docs/tutorials/20_conformal_prediction.ipynb
+++ b/nbs/docs/tutorials/20_conformal_prediction.ipynb
@@ -106,7 +106,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e95f40e90284ad88509b20e11bde568",
+       "model_id": "f4e4d84cd6a44714adb9be9ab9c030e5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -120,7 +120,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4ddd3ee5efe47e69eb3357af61c51fb",
+       "model_id": "6bac6c0e19db44a2a666478c874ddf91",
        "version_major": 2,
        "version_minor": 0
       },
@@ -134,7 +134,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25c3c9aadfe54f41ac608c01284a3a4e",
+       "model_id": "54696aa66dfb4709a8995b575df67913",
        "version_major": 2,
        "version_minor": 0
       },
@@ -148,7 +148,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f39126dacfbe4081835ae886d7c4efc8",
+       "model_id": "751c488241db48d38d7f18bd04e5266f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -162,7 +162,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e65e255619774a4db3945b7b41d96af7",
+       "model_id": "84b8c306a4f24c9e818358a80655a725",
        "version_major": 2,
        "version_minor": 0
       },
@@ -176,7 +176,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e830c80706994e1ab41c846ce8aa437d",
+       "model_id": "ce85b06b6fdc4511b2dc8e2cc4a6b33e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -190,7 +190,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f7418b413e44e42a1eb4c0dab8d321e",
+       "model_id": "8e012f71edc24be9b57e7cc1dc9bf40b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -204,7 +204,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc5a3118e2b44a91abd23f92ac4f3bfd",
+       "model_id": "c3973795a3bf4d79a09b9795426008cf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -218,7 +218,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c029b2370e494a7a93f99989ec4a5304",
+       "model_id": "7465f0cfebda4bd7995ed597dc1533d6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -232,7 +232,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2a862454b834645a454e2134d12a715",
+       "model_id": "79b005fcb65243de80fb4e9d6a67a52d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -246,7 +246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "036eaffe339645f8a7723cefede335f2",
+       "model_id": "c376fe4c0ff34093adbddc70b5fd451c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -260,7 +260,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "655e0ef54144434798b2b7b40122e6b7",
+       "model_id": "a99d7bbefbcc4552bf0f5be71387bfc6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -274,7 +274,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cda10db85e0940cbad25cc92f84debc1",
+       "model_id": "b46fea3664324f699324126ceff0d3f8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -288,7 +288,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7af7c58c97e94236bbc136cd6b451c45",
+       "model_id": "a53502ef6af441439c06900e58d35373",
        "version_major": 2,
        "version_minor": 0
       },
@@ -306,8 +306,8 @@
     "\n",
     "prediction_intervals = PredictionIntervals()\n",
     "\n",
-    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100, loss=MAE()), \n",
-    "          NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[90]))]\n",
+    "models = [NHITS(h=horizon, input_size=input_size, max_steps=100, loss=MAE(), scaler_type=\"robust\"), \n",
+    "          NHITS(h=horizon, input_size=input_size, max_steps=100, loss=DistributionLoss(\"Normal\", level=[90]), scaler_type=\"robust\")]\n",
     "nf = NeuralForecast(models=models, freq='ME')\n",
     "nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)"
    ]
@@ -329,7 +329,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50872a892eff4a8aa112e895186e9813",
+       "model_id": "8f761ac5838b46d3983a802a5c87037b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -343,7 +343,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "09d733fc4dd74a1ea4232df550511bd0",
+       "model_id": "e1c43ff39b434340bdc882323ef2f85c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -366,7 +366,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Dhw7VqhhJe6TdfZ+UlITffvstR8cxNDTMctvJkydrFuUOCQmBn58fzp49iylTpmglXgBg0aJFuHz5MszMzLB69WoEBgZCKpUiOjpas4+0apGMqijWrFmDly9fYvHixWjfvj1sbW3x+vVr/Pzzz6hTp45WRUSaKVOmICAgAKtXr0bXrl1RvHhxBAUFYceOHfjqq6/Qs2dPrQRI2nRRFStWzLTy4r+P/1Yg5dSH71kkEmWp3Yc+nOoqLCwsS7EPGjQow319agy0atUKAQEB2LVrFwYOHAhPT0/Ex8fj5MmT+O6771CzZs101Rh56WPnKyvtPvZ+c2tMZDXGNN9//z2CgoJQqlQpHDp0CNHR0UhOTkZERATCwsK0zu9/x4StrS0uXbqE69evY+rUqWjUqBGMjIzg4+OD+fPnw9PTE/v3789WPERERERUdDD5QkRERERFyrZt27LV/nOmHssLadUqP/30E7y8vODu7p7ugvSn1mcpV64cZsyYgdOnTyM6Ohq3bt3STLW2du1anDhxIl0fV1dXeHl54dixYwgPD8fjx48xdOhQAMDhw4exceNGTVtnZ2cAqVOA5dV6Jpn5sPriY9OjZfZaWuwA4Ovrm3uBZcLCwgLfffcdduzYAT8/PwQFBWHp0qUwNTXVqojJS2nn7P379x9tl5a0dHR0zPYxPmdM2NvbayqA3r59m+V+79+/16x7tH//fnzzzTfp1gDKylpGadVfN27cQFxcHH7//XdUrVoVEokEgwcPznIlDxEREREVLUy+EBEREVGRERkZqUksHD58GImJiZk+0tZN+euvv/DixQtdhq0l7QJ5zZo1M3z97du3eP36dZb3Z2BggPr16+Pw4cNwd3cHAJw/f/6T/apWrYotW7Zo1jX5sE/aNrlcjmPHjmU5ltxQpkwZzULpV65cybRdZq9VqVJFs+5JWqIrP5UoUQJTp07VLEqflc/ic324lkt8fHyGbeLi4rTWhsmuzxkTRkZG+PLLLwEAJ0+ezHK/D5NJmf2+XLhwIVuxmJqa4uuvv8bRo0cBAFKpVLOeDQDNVGZA5tVVRERERFQ0MPlCREREREXG7t27oVAoYGNjg86dO8PS0jLTR926dVGxYkUABav6xcbGBgDw6NGjDF+fPn16pn1lMlmmrxkaGmoWS/9w+qiP9QEAMzOzdH3q1Kmjudg9a9asT66dkrbAfW4QiUTo3r07AGDTpk2IjY1N1+bVq1eZTidnZGSEwYMHAwB27typdWE9IzmNPSfnNa/06NEDRkZGkEqlWLp0aYZtFi9eDJlMBrFYjB49emT7GJ87JoYMGQIAOH36NE6fPp2lY6b9rgAZ/74kJiZi4cKFGfZVKpVQq9WZ7jvt8wG0P6O0xB2QmrAiIiIioqKLyRciIiIiKjLSkihdunTRJBo+pmfPngCAXbt2QalU5mlsWZW2Hs3ChQtx9OhRTVwBAQHo27cvfvvtN9jZ2WXYt169ehg3bhyuXLmiNfVTSEgIxo4dq6mY6dChg+a1rl27YvDgwThz5ozWxeSYmBgsXLgQFy9eTNdHJBJh06ZNMDExwbt371CvXj0cPnxYa2H64OBg7NmzB61bt8a0adM+86xomzlzJszMzBAeHo42bdrgwYMHAFIrES5duoS2bdvC3Nw80/6zZ89G2bJloVQq0a5dO6xatUorWRAfH4+zZ89i4MCBaNKkSY5iXLp0Kdq3b4/du3drLQYvk8nw22+/Yfny5QC0z2teKVGiBMaPHw8AWLJkCebMmaP5rOPi4jB79mxNPBMnToSLi0u2j/G5Y+K7775D48aNIQgCevTogeXLlyMqKgpA6noyb9++xerVq7X6Va5cWVPNNXjwYPj4+Gheu3XrFpo3b55hcg5InWLN09MTCxcuxIMHD7R+/x8/foz+/fsDSJ02rmnTpprXbG1tUaJECQDA9u3bC8z3BhERERHpgEBEREREVATcunVLACAAEE6ePJmlPo8fP9b0OX78uGb7nDlzBABCs2bN0vUZOHCgAEAYOHDgJ/eftu85c+Zk8V0Iwtu3bwUnJydNXyMjI8HGxkbzfPHixUKzZs0y3K+Hh4emnUgkEmxtbQULCwvNNgDChAkTtPqk7SvtYW1tLVhbW2tt++abbwSVSpUu1nPnzgn29vaadoaGhoK9vb1gbm6u1X/o0KFa/S5fvqx5LSvn7/Lly+leO3TokGBkZKRpY2VlpTluiRIlhO3btwsABBMTkwz3/ebNG6F69epacdra2qZ77+XKlctWXGnSxlDaw8zMTChWrJggEok02ypVqiSEhoZ+9Bz814fn7mPH/y+ZTCb06tVL09fAwECws7MTDAwMNNv69OkjyOXydH2zM+ZzOiYEQRAiIyOFJk2apBvDYrFYs61Lly5afU6ePKk1DszNzTXHMjc3Fy5cuJDh+QoICNCKx9DQUChWrJhgbGys2WZsbCwcOnQoXZwLFizQtDExMRFKliwpeHh4CL179/7k+SEiIiKiwoOVL0RERERUJGzbtg1A6lREbdq0yVKfqlWrolKlSlr9dc3DwwP37t3DkCFD4OrqCiB1HYpOnTrhzz//xIwZMzLte+DAAcybNw8tW7ZE6dKlIZfLoVAo4OHhgd69e+PixYtYtWqVVp/169dj6dKl6NChAzw9PSEIAiQSCVxdXfH111/jyJEjOHTokNZaF2lat26N169fw9vbG40bN4aNjQ3i4uJgYGCAypUrY8iQIThx4gTWr1+fuycJwDfffIN79+6hZ8+ecHR0hEwmg5OTE8aPH48HDx5opqRKWx/mv0qXLo179+5h165d6NSpE1xcXJCcnAy5XI7SpUujW7du+PXXX3Hr1q0cxTd8+HBs3rwZffr0QZUqVWBubo6EhATY2dmhSZMmWLNmDe7fv69ZqD6vGRsb4+DBgzhy5Ajat28Pe3t7JCYmwt7eHu3bt8fRo0exb98+zcL3OfU5Y8LBwQFXrlzBnj170L59ezg6OiI5ORl2dnaoXbs2pk+fjsWLF2v16dSpE65du4aOHTvC1tYWSqUSDg4O+P7773H//n20bNkywzhLlCiBEydOYMKECahfvz5cXFyQlJQEIyMjVK5cGaNHj8aTJ0/wzTffpOs7c+ZMrF27FnXq1IFYLEZQUBACAwMRFhb2WeeOiIiIiPSLSBC4CiARERERERUts2bNwuLFi/HVV19ppk4jIiIiIiLKLax8ISIiIiKiIiUyMhJbt24F8O8aOkRERERERLmJlS9ERERERFTorFu3DikpKfjmm29QqlQpGBkZQSaT4eLFi5g0aRJevHgBR0dHPH/+HPb29roOl4iIiIiIChkmX4iIiIiIqNDx8vLC2rVrAQCGhoawsbFBQkIClEolgNS1f44fP47mzZvrMEoiIiIiIiqsjHQdABERERERUW4bOHAgDA0Nce3aNQQHByM6OhpmZmYoXbo02rZti/Hjx6NEiRK6DpOIiIiIiAopVr4QERERERERERERERHlIgNdB0BERERERERERERERFSYcNqxj1Cr1QgJCYGVlRVEIpGuwyEiIiIiIiIiIiIiIh0SBAGJiYlwdXWFgUHm9S1MvnxESEgISpYsqeswiIiIiIiIiIiIiIioAHn//j3c3NwyfZ3Jl4+wsrICkHoSra2tdRwNAYBCocC5c+fQpk0biMViXYdDlCmOVdI3HLOkLzhWSR9x3JK+4FglfcRxS/qE45X0DcdsxhISElCyZElN/iAzTL58RNpUY9bW1ky+FBAKhQLm5uawtrbmLzwVaByrpG84ZklfcKySPuK4JX3BsUr6iOOW9AnHK+kbjtmP+9RSJZlPSEZERERERERERERERETZxuQLERERERERERERERFRLmLyhYiIiIiIiIiIiIiIKBdxzZfPJAgClEolVCqVrkMpEhQKBYyMjCCVSj96zg0NDWFkZPTJefeIiIiIiIiIiIiIiHIbky+fQS6XIzQ0FCkpKboOpcgQBAHOzs54//79JxMr5ubmcHFxgbGxcT5FR0RERERERERERETE5EuOqdVqBAQEwNDQEK6urjA2NmaVRT5Qq9VISkqCpaUlDAwynjVPEATI5XJERkYiICAAnp6embYlIiIiIiIiIiIiIsptTL7kkFwuh1qtRsmSJWFubq7rcIoMtVoNuVwOU1PTjyZUzMzMIBaLERgYqGlPRERERERERERERJQfWA7wmVhRUXDxsyEiIiIiIiIifaZWC7oOgYiIcoiVL0RERERERERERAXQ+efhsDIxQmVXa9iac01bIiJ9wuQLERERERERERFRAROXIkd0UurjbXQKXG1NUdnVGsWtOLU6EZE+YPKFiIiIiIiIiIiogPGPTNZ6HhInRUicFI5WJqjsao0StmY6ioyIiLKCi2IUISKR6KOPQYMG6TpEIiIiIiIiIqIiT60W8DYqOcPXIhNluPoyEqd9Q/E2KhmCwHVhiIgKIla+FCGhoaGanw8ePIiffvoJL1++1GwzM9O+Y0KhUEAsFudbfEREREREREREBATHSSBTqj/aJi5FgZv+0XgUFIfKLtYo42gJQwNRPkVIRESfwsqXXCIIApKTk3XyyOodDs7OzpqHjY0NRCKR5rlUKoWtrS1+++03NG/eHKamptizZw/mzp2LGjVqaO1nzZo1KFWqlNa27du3o1KlSjA1NUXFihXx888/59KZJSIiIiIiIiIqWvwjk7LcNlmmwt23sTjxKBhPQ+Ih/0TShoiI8gcrX3JJSkoKLC0tdXLspKQkWFhY5Mq+pk2bhpUrV2L79u0wMTHB5s2bP9lny5YtmDNnDjZs2ICaNWviwYMHGDZsGCwsLDBw4MBciYuIiIiIiIiIqCiQyFUIjZfmoJ8aj97H41lIAjydrFDR2QqmYsM8iJCIiLKCyRfS4uXlhe7du2erz4IFC7By5UpNv9KlS+PZs2f45ZdfmHwhIiIiIiIiIsqGgKhkfM4yLgqVgGchCXgZloAyjpao5GINSxNeAiQiym/85s0l5ubmSErKeklobh87t9SpUydb7SMjI/H+/XsMGTIEw4YN02xXKpWwsbHJtbiIiIiIiIiIiIqCN1G5c31JpQZehSfhdUQSPIqZo7KrNWzNjXNl30RE9Gm5nnxJSEiAn58f3Nzc4OzsnNu7L7BEIlGuTf2lS/99DwYGBunWlFEoFJqf1erUeUS3bNmCevXqabUzNGRpKxERERERERFRVkUmypAgUebqPgUBeBudgrfRKXC1NUVlV2sUtzLN1WMQEVF6BjnpdO7cOQwePBgPHjzQ2r5x40Y4OTmhXr16cHNzw+TJk3MlSNIdR0dHhIWFaSVgHj58qPnZyckJJUqUwJs3b1CuXDmtR+nSpXUQMRERERERERGRfnoTmbezqoTESXHhWQTOPwtHcJwkT49FRFTU5Sj5snXrVhw8eBDlypXTbHv27BnGjh0LlUqF+vXrw9raGqtXr8bJkydzLVjKf82bN0dkZCSWLVsGf39//O9//8OZM2e02sydOxfe3t5Yu3Yt/Pz84Ovri+3bt2PVqlU6ipqIiIiIiIiISL8oVWoExqTky7EiE2W4+jISZ3xD8TYqOd2sJ0RE9PlylHy5f/8+atasCSsrK8227du3QxAE7NixA3/99RcePHgAExMT/Pzzz7kWLOW/SpUq4eeff8b//vc/VK9eHXfu3ElX0TR06FBs3boVO3bsQNWqVdGsWTPs2LGDlS9ERERERERERFn0LiYFSlX+JkFiUxS46R+Nk49D8So8ESo1kzBERLklR2u+hIeHp1uY/cKFC7C1tcW3334LAPDw8EDTpk3x9OnTz4+Sct2gQYMwaNAgzfNSpUplepfDyJEjMXLkSK1tM2fO1Hret29f9O3bN9fjJCIiIiIiIiIqCt5EJuvs2ElSJe6+jcWTkHh84WqD8k5Wn+5EREQflaPKFyMjI8jlcs3zpKQkPHnyBE2aNIGBwb+7dHR0RGRk5OdHSUREREREREREVEglShWISJTpOgxI5GrcexuL2GT5pxsTEdFH5Sj5UqpUKfj4+Gie//HHH1CpVGjdurVWu+joaNjb239ehERERERERERERIVYQJTuql4y8jAoTtchEBHpvRwlX7799lu8f/8ePXr0wLp16zBp0iQYGxuja9eumjaCIMDHxwdlypTJrViJiIiIiIiIiIgKFUEQClzyJTROiogEqa7DICLSazlKvowdOxYNGjTAsWPH4OXlhbCwMCxZsgQlSpTQtLl06RIiIyPRokWLXAuWiIiIiIiIiIioMAlLkCJZptJ1GOk8eB+n6xCIiPSaUU46mZub4/r167h+/ToiIiJQo0YNeHp6arUxNDTE6tWr0blz51wJlIiIiIiIiIiIqLB5E1mwql7SRCfJ8T4mBSWLmes6FCIivZSjypcTJ07gzz//RLNmzdCzZ890iRcAaN68OcaPH5+jaceUSiV+/PFHlC5dGmZmZihTpgzmz58PtVqtaSMIAubOnQtXV1eYmZmhefPmePr0qdZ+ZDIZxo4dCwcHB1hYWODrr79GUFBQ9t8wERERERERERFRLpMpVQiKTdF1GJl6HBQPQRB0HQYRkV7KUfKlW7duWLduXW7HorF06VJs2rQJGzZswPPnz7Fs2TIsX74c69ev17RZtmwZVq1ahQ0bNuDu3btwdnZG69atkZiYqGnj5eWFY8eO4cCBA7hx4waSkpLQqVMnqFQFr5STiIiIiIiIiIiKlsDoFKjUn26nK/ESRYFbj4aISF/kKPni6OgIOzu73I5F49atW+jSpQs6duyIUqVK4ZtvvkGbNm1w7949AKlVL2vWrMGsWbPQvXt3VKlSBTt37kRKSgr27dsHAIiPj8e2bduwcuVKtGrVCjVr1sSePXvg6+uLCxcu5FnsREREREREREREWfEmMknXIXySb3A8VGpWvxARZVeO1nxp3rw57ty5A0EQIBKJcjsmNG7cGJs2bYKfnx/Kly+PR48e4caNG1izZg0AICAgAGFhYWjTpo2mj4mJCZo1a4abN29ixIgR8PHxgUKh0Grj6uqKKlWq4ObNm2jbtm2648pkMshkMs3zhIQEAIBCoYBCodBqq1AoIAgC1Gq11nRolLfSSl3Tzv3HqNVqCIIAhUIBQ0PD/AiPSCPtO+O/3x1EBRXHLOkLjlXSRxy3pC84Vkkf6fO4jZfIEZMo1XUYn5QsUeFFSCzKO1npOhS9p8/jlYomjtmMZfV85Cj5smDBAtStWxcTJkzAkiVLYGpqmpPdZGratGmIj49HxYoVYWhoCJVKhUWLFqFPnz4AgLCwMACAk5OTVj8nJycEBgZq2hgbG6er0HFyctL0/y9vb2/Mmzcv3fZz587B3Fx7cTEjIyM4OzsjKSkJcrk8Z2+0kOvUqROqVq0Kb29vAEC1atUwatQojBo16rP3/eH0cpmRy+WQSCS4du0alErlZx+TKCfOnz+v6xCIsoVjlvQFxyrpI45b0hccq6SP9HXcWug6gCx6HQG81nUQhYi+jlcqujhmtaWkZG2trhwlX/bv348OHTpg/fr1OHDgAFq1agV3d/cMkzAikQizZ8/O1v4PHjyIPXv2YN++ffjiiy/w8OFDeHl5wdXVFQMHDtTa94eyUonzsTYzZszAxIkTNc8TEhJQsmRJtGnTBtbW1lptpVIp3r9/D0tLy1xPPhUWRkZGMDY21py7u3fvwsLCIl0iKzsEQUBiYiKsrKw++VlLpVKYmZmhadOm/Iwo3ykUCpw/fx6tW7eGWCzWdThEn8QxS/qCY5X0Ecct6QuOVdJH+jpu1WoBpx6HQqbUn9lUKrta4QtXG12Hodf0dbxS0cUxm7G0GbM+JUfJl7lz50IkEkEQBERERGjWWclITpIvU6ZMwfTp0/Htt98CAKpWrYrAwEB4e3tj4MCBcHZ2BpBa3eLi4qLpFxERoamGcXZ2hlwuR2xsrFb1S0REBBo2bJjhcU1MTGBiYpJuu1gsTje4VCoVRCIRDAwMYGCQo6VzioS0cwSkr1TKibSpxj7cb2YMDAwgEoky/PyI8gvHH+kbjlnSFxyrpI84bklfcKySPtK3cfs+JgUytQgw0J9p0v0iJajoagdTsf7EXFDp23gl4pjVltVzkaPky/bt23PSLctSUlLSXVg3NDTUXHgvXbo0nJ2dcf78edSsWRNA6hRTV69exdKlSwEAtWvXhlgsxvnz59GrVy8AQGhoKJ48eYJly5blesyCAGSx2ijXmZsD2Vl6p3nz5qhatSoMDQ2xc+dOGBsbY8GCBejXrx/GjBmDw4cPo3jx4tiwYQPat28PAHj27BkmT56Ma9euwcLCAm3atMHq1avh4OAAAEhOTsaoUaNw9OhRWFlZYfLkyemOW6pUKXh5ecHLywsAsGrVKmzfvh1v3rxBsWLF0LlzZyxbtgyWlpYAgB07dsDLywsHDx6El5cX3r9/j0aNGmHt2rXpKpGIiIiIiIiIiPSFf2SSrkPINqVKwNOQeNT2KKbrUIiI9EKOki8fTv2VFzp37oxFixbB3d0dX3zxBR48eIBVq1Zh8ODBAFKrHry8vLB48WJ4enrC09MTixcvhrm5Ofr27QsAsLGxwZAhQzBp0iTY29ujWLFimDx5MqpWrYpWrVrleswpKcA/OYN8l5QEWGRzktCdO3di6tSpuHPnDg4ePIhRo0bh+PHj6NatG2bOnInVq1fju+++w7t37xAfH49mzZph2LBhWLVqFSQSCaZNm4ZevXrh0qVLAFKrlS5fvoxjx47B2dkZM2fOhI+PD2rUqJFpDAYGBli3bh1KlSqFgIAA/PDDD5g6dSp+/vlnTZuUlBSsWLECu3fvhoGBAfr374/Zs2fj4MGDOTlVREREREREREQ6JZGrEBov1XUYOfIqPAkVnK1haZKjS4pEpEfex6RALs/awvKUsQL5Tbl+/XrMnj0bP/zwAyIiIuDq6ooRI0bgp59+0rSZOnUqJBIJfvjhB8TGxqJevXo4d+4crKysNG1Wr14NIyMj9OrVCxKJBC1btsSOHTtgaMjyyOrVq+PHH38EkLrWzZIlS+Dg4IBhw4YBAH766Sds3LgRjx8/xunTp1GrVi0sXrxY0//XX39FyZIl4efnB1dXV2zbtg27du1C69atAaQmd9zc3D4aQ1oFDJBazbRgwQKMGjVKK/miUCiwadMmlC1bFgAwevRozJ8/P1fOARERERERERFRfnsTlQRB0HUUOaMWgMdBcWhY1kHXoRBRHkmRK3H3bSyCYyWoVkJH1QaFxGclX5RKJU6dOoW7d+8iKioK9erV01SnhISEICoqCpUrV4aRUfYOY2VlhTVr1mDNmjWZthGJRJg7dy7mzp2baRtTU1OsX78e69evz9bxc8LcPLUCRRdysn59tWrVND8bGhrC3t4eVatW1WxLW58lIiICPj4+uHz5smY6sA/5+/tDIpFALpejQYMGmu3FihVDhQoVPhrD5cuXsXjxYjx79gwJCQlQKpWQSqVITk6GxT+lPObm5prEC5C6lk9kZGT23zARERERERERUQEQEJWs6xA+S2B0Ciq7yGFrbqzrUOgTBEGAKDtrFVCRJggC/MKT8CgoDkqVnmaIC5gcJ1+uXr2K7777DsHBwZpfZIVCoUm+XLx4EYMGDcJvv/2GHj165FrABZVIlP2pv3Tpv4sCpS1M/+FzIHWBe7Vajc6dO2vW0/mQi4sLXr16le3jBwYGokOHDhg5ciQWLFiAYsWK4caNGxgyZAgUin/L2TKKU9DX20OIiIiIiIiIqEiLSJQiQaLUdRifRRCAR0HxaFbeUdeh0EfEJMtxJyAarrZmqOZmq+twqICLTZbj74AYxCTLdR1KoWLw6Sbp+fr6okOHDoiIiMD48eNx6NChdBfEe/ToAXNzcxw5ciRXAiXdqVWrFp4+fYpSpUqhXLlyWg8LCwuUK1cOYrEYt2/f1vSJjY2Fn59fpvu8d+8elEolVq5cifr166N8+fIICQnJj7dDRERERERERKQTAZH6XfWSJjhWgohE/Vy3prBTqNTwCYzFn0/DEJOswJPgBDwPTdB1WFRAKVVqPHiXNl6YeMltOap8mT9/PmQyGc6dO4evvvoqwzbm5uaoVKkSHjx48FkBku6NHj0aW7ZsQZ8+fTBlyhQ4ODjg9evXOHDgALZs2QJLS0sMGTIEU6ZMgb29PZycnDBr1iwYGGSe2ytbtiyUSiXWr1+Pzp0746+//sKmTZvy8V0REREREREREeUfpUqNwJgUXYeRax69j0fryqa6DoM+EBSbAp/AWCTLVFrbH7yLg9jQAOWKc/0O+ldovAR3AmLSjRfKPTmqfLl69Srq16+faeIljbu7O6sZCgFXV1f89ddfUKlUaNu2LapUqYLx48fDxsZGk2BZvnw5mjZtiq+//hqtWrVC48aNUbt27Uz3WaNGDaxatQpLly5FlSpVsHfvXnh7e+fXWyIiIiIiIiIiylfvYlIK1ToKkYkyBMdJdB0GAZDIVbj+KhLX/KIyvZB+920MAqMLR+UVfR6pQoWbr6Nw+UUkEy95LEeVLwkJCShRosQn28lkMqhU/AALmitXrqTb9vbt23TbPpxKztPTE0ePHs10n5aWlti9ezd2796t2TZlypSPHmPChAmYMGGC1rbvvvtO8/OgQYMwaNAgrde7du2K2NjYTOMgIiIiIiIiIiqI3hSSKcc+9Oh9HErYmuk6jCJLEAS8ikjCo/dxUHwisScIwC3/aBgZGvAzK8JeRyTh4fs4yJVqXYdSJOQo+eLi4oLnz59/st2TJ0/g4eGRk0MQEREREREREREVColSBSISZboOI9fFpSjwNioZpRwsdB1KkROXkrpAenRS1tfpUAvAX6+i0LyCI4pbc8q4oiReosDdgJhC+T1UkOVo2rE2bdrg6dOnOHbsWKZtduzYgcDAQHTs2DHHwREREREREREREem7wlj1kuZRUBzU6sIznVpBl7ZA+tknYdlKvGj6qwVc8YtEdBIvwhcFarUA36B4nPENZeJFB3KUfJk5cyYsLS3Rp08fzJ49G/fu3QMApKSk4MmTJ1i4cCF++OEH2NvbY+LEibkaMBERERERERERkb4QBAEBUYU3+ZIsU+F1ZJKuwygSQuMl+MM3FM9DE/E5+S6lSsCVl5GIT1HkXnBU4EQkSHH6SSh8g+M/a7xQzuUo+eLh4YE//vgDdnZ2WLRoEerVqweRSIRDhw6hevXq+Omnn2BlZYXff/8dzs7OuR0zERERERERERGRXghLkCJFXrjXRH4SHA+limtI5JW8WCBdplTj0stwJEqZgClsZEoV/n4TjQvPI5AgUeo6nCItR2u+AEDjxo3h5+eHbdu24cKFC3j79i1UKhXc3NzQqlUrjBgxAra2trkYKhERERERERERkX7xjyi8VS9ppAo1XoQlokoJG12HUujk5QLpErkal15EoHVlJ5gb5/gyMRUggdHJ8AmMhVTBZGhB8Fm/VVZWVvDy8oKXl1cuhUNERERERERERFQ4yJQqBMel6DqMfPE8NAHlilvCVGyo61AKhXiJAncCYhCZx+t0JMtUuPQiAq0qOfGz02NJMiXuvo1BaJxU16HQB3I07RgRERERERERERF9XGB0CorKbFwKlYBnoQm6DkPvqdQCHgfF4YxvaJ4nXtIkSJS48jIiT6prKG+p1QKehybg9ONQJl4KoBxVvrx79y5L7YyNjWFnZwcTE5OcHIaIiIiIiIiIiEhvvSliC9G/Ck9EBScrWJhwCqucCE+Q4k5ADBKl+b9OR0yyAlf9ItGigiOMDHm/vj6ITpLhTkAMYlO4bk9BlaNvwlKlSkEkEmWprUgkQoUKFdCvXz9MmjSJiRgiIiIiIiIiIir0YpPliEkuWhdFVWrANzge9cvY6zoUvSJVqPDgXRwConS7PlBkogzXX0ehmacjDAyydu2X8p9CpcbjoDj4hSdBEHQdDX1MjpIvTZs2hUKhwK1btwAA9vb2cHd3hyAICAoKQlRUFACgfv36iIqKwqtXrzB79mwcP34cV65cgbm5ee69gwJm399ZqwrKLX3ruWerffPmzVGjRg2sWbMmbwLKwKBBgxAXF4fjx4/n2zGJiIiIiIiIiHTpTVTRqnpJExCVjEou1rAxE+s6FL3wJjIJD97FQVZApvwKjZPipn80GpWzz/LN95R/gmJT4BMYi2SZStehUBbkqIbs1KlTUCqVqF27Ni5duoTIyEj4+Pjg/v37iIiIwKVLl1C3bl0olUrcv38fAQEBaNmyJXx8fLBq1arcfg9UCPz222+oUaMGzM3N4eHhgeXLl6drc/XqVdStWxfOzs4oV64cNm3apINIiYiIiIiIiIg+Tq0W8DYqRddh6IQgAI/ex+k6jAIvQarAxefhuP0mpsAkXtK8i0nB3wExug6jSJMr1UiUKhCRKMX7mBS8Ck/ENb9IXPOLYuJFj+So8mX27Nl48+YNXr9+DRsbm3SvN2/eHH/++Sc8PT0xa9YsrF27Fnv37oWnpycOHz6MH3/88bMDp8LjzJkz6NevH9avX482bdrg+fPnGDp0KMzMzDBmzBgAQEBAADp06IChQ4fi559/xqNHjzBmzBg4OjqiR48eOn4HRERERERERET/CoqVFLgL6vkpKFaCqCQZHCy5/EBGnocm4Hl4MlQFeIi8iUyG2NAAtT3sdB1KoSAIAmRKNWQKNaRKFSRyFWRKNSQKFWQKFSQKFaQKNWRKFWQKNZRqzidWGOSo8uXw4cNo0aJFhomXNLa2tmjRogWOHj0KAHB0dEStWrXg7++fs0gpT8jlckydOhUlSpSAhYUF6tWrhytXrgAA4uPjYWZmhrNnz2r1OXr0KCwsLJCUlFo+GxwcjN69e8POzg729vbo0qUL3r59m+UYdu/eja5du2LkyJEoU6YMOnbsiGnTpmHp0qUQ/pm4cNOmTXB3d8fq1atRoUIFDB06FIMHD8aKFSty5TwQEREREREREeUW/yI65diHWP2SXmSSDADwJDihQCde0rwMS4RvULyuwyiw1GoBKXIlYpLlCImT4E1kEp6FJOD+u1jcfB2Fyy8icNo3FMceBOHA3fc4ej8Yf/iG4uLzCNz0j4ZPYCyehSTAPzIZIXFSxCTLkSxTMfFSiOSo8iUqKgoSieST7SQSiWb9FwBwcnKCSsWyqILk+++/x9u3b3HgwAG4urri2LFjaNeuHXx9feHp6YmOHTti7969aNeunabPvn370KVLF1haWiIlJQUtWrRAkyZNcO3aNRgZGWHhwoVo164dHj9+DGNj40/GIJPJ0q0DZGZmhqCgIAQGBqJUqVK4desW2rRpo9Wmbdu22LZtGxQKBcRiziNKRERERERERLonkasQFi/VdRg6F54gQ2i8BC42ZroORefkSjUevo/D67B4WOg6mGzyDY6H2EiEis7Wug6lwBAEAX8HxOBNZLKuQ6ECLkeVL2XLlsXFixfh5+eXaRs/Pz9cvHgRZcuW1WwLDQ2Fg4NDTg5JecDf3x/79+/HoUOH0KRJE5QtWxaTJ09G48aNsX37dgBAv379cPz4caSkpM5TmpCQgD/++AP9+/cHABw4cAAGBgbYunUrqlatikqVKmH79u149+6dpoLmU9q2bYujR4/i4sWLUKvV8PPzw5o1awCkjhkACAsLg5OTk1Y/JycnKJVKrQQfEREREREREZEuvYlKgsAb1wGw+gUAguMkOO0bitcR+lsNdT8wDv6R+ht/bhIEAbfeRDPxQlmSo+TL6NGjIZVK0aRJEyxfvhwBAQFQKBRQKBQICAjA8uXL0bRpU8hkMvzwww8AAKlUigcPHqBWrVq5+gYo5+7fvw9BEFC+fHlYWlpqHlevXtVMD9exY0cYGRnhxIkTAIAjR47AyspKU4Xi4+OD169fw8rKStO/WLFikEqlWZ5ibtiwYRgzZgw6deoEY2Nj1K9fH99++y0AwNDQUNNOJBJp9Uubkuy/24mIiIiIiIiIdIUXZf8Vk6xAYHTRPB9ShQo3X0fh6stIpMj1fyagOwExeBedouswdEqtFnDTPxpvo4r2eaCsy9G0Y6NGjcKLFy+wfv16TJ8+HdOnT9dcAE+7IC4IAkaPHq1JvgQGBqJv375cHL0AUavVMDQ0hI+Pj1aSAwAsLS0BAMbGxvjmm2+wb98+fPvtt9i3bx969+4NIyMjzT5q166NvXv3ptu/o6NjluIQiURYunQpFi9ejLCwMDg6OuLixYsAgFKlSgEAnJ2dERYWptUvIiICRkZGsLe3z9b7JiIiIiIiIiLKCxGJUiRKlboOo0B5HBSPknbmMDAoOjfPvo1Khk9gLGRKPVjYJYsEAbjpHwUjQ0e42ha9qeTSEi/vYph4oazLUfIFANauXYtevXph48aNuHXrFkJCQgAALi4uaNiwIYYPH46mTZtq2leoUAEbN278/Igp19SsWRMqlQoRERFo0qRJpu369euHNm3a4OnTp7h8+TIWLFigea1WrVo4ePAgihcvDmvrz5v70dDQECVKlAAA7N+/Hw0aNEDx4sUBAA0aNMDJkye12p87dw516tThei9EREREREREVCCw6iW9RKkSb6KSUK64la5DyXMpciXuvo1FcOyn18rWR2oBuPEqCs0rOKK4tamuw8k3arWAG6+jEFRIP1fKOzlOvgBAo0aN0KhRo9yKhfJZ+fLl0a9fPwwYMAArV65EzZo1ERUVhUuXLqFq1aro0KEDAKBZs2ZwcnJCv379UKpUKdSvX1+zj379+mH58uXo0qUL5s+fDzc3N7x79w5Hjx7FlClT4Obm9sk4oqKicPjwYTRv3hxSqRTbt2/HoUOHcPXqVU2bkSNHYsOGDZg0aRK+/fZb+Pr6Ytu2bdi/f3/unxgiIiIiIiIiomxSqtS8Kz4TvsHxKGVvASPDHK2AoBdeRyTiwbs4KFSFe8EfpVrAVb9ItKzkhGIWxroOJ8+p1AKuv4pESJxU16GQHvqs5Aul17eeu65DyJbt27dj4cKFmDRpEoKDg2Fvb48GDRpoEi9A6rRgffr0wfLly/HTTz9p9Tc3N8e1a9cwbdo0dO/eHYmJiShRogRatmyZrUqYnTt3YvLkyRAEAQ0aNMCVK1fw5Zdfal4vXbo0Tp8+jQkTJuDnn3+Gq6sr1q1bx2nsiIiIiIiIiKhACIxJgbKQX3jPKYlcjZfhifjC1UbXoeS6RKkCdwJiEJ4g03Uo+UahEnD5RQRaVXKCjXnhnZFGpRZw7VUkQpl4oRz67OSLUqlEdHQ0ZLLMv2Dc3fUrIVGYXblyReu5WCzGvHnzMG/evI/2W7ZsGZYtW5bha87Ozti5c2emfXfs2PHRfTs4OODWrVsfbQOkVuDcu3cPCQkJsLa2hoFB4b1bgoiIiIiIiIj0C6cc+7hnIQkoV9wSJkaGn26sBwRBwIuwRPgGxUOpLnpJN5lSjUsvw9GqkhOsTAtfAkapUuP6qyiExjPxQjmX4+TLhQsXsHDhQty+fRsKhSLTdiKRCEolFxojIiIiIiIiIqLCKUGqQGRi0al8yAmFSsDz0ETUKGmr61A+W1yKHH8HxCA6Sa7rUHRKIlfj0osItKnsDDPjwpFUA1ITL1f9IotUNRPljRwlX06dOoVu3bpBpVLBzs4OZcqUgaWlZW7HRkREREREREREVOCx6iVr/MISUcHJSm8v1KvVAp6GJOBpSDyKYLFLhpJlKlx6EYGWlYrDVKyfn+uHFCo1rr6MRASTqZQLcpR8mTdvHtRqNdasWYPRo0fD0FD/f7GIiIiIiIiIiIiySxAEvI1i8iUrlGoBvsHx+LJ0MV2Hkm1RSTLcCYhBXErmMwAVVfESBa68jMRXFYvD2Eh/lwlQqNS48jKSVWyUa3L02/D06VM0aNAA48aNY+KFiIiIiIiIiIiKrNB4KVLkKl2HoTfeRCYhQao/CQylSo3772Jx/lk4Ey8fEZMsxzW/SChVal2HkiNyZeoUaky8UG7KUfLF0tISTk5OuR2LXhIE1hgWVPxsiIiIiIiIiCivccqx7FELwOP38boOI0vCE6Q4/SQML0ITwctMnxaRKMP111FQ69mcbDJl6tRpRX0NH8p9OUq+tGrVCvfv34darZ+ZzNwgFosBACkpKTqOhDKT9tmkfVZERERERERERLlJqlAhKJbXhrLrXUwKYpIL7oVuuVKNv99E4+LzCCRJlboOR6+Exknx59MwhMVLdR1KlkgVKlx+EVGgxyPprxyt+bJ06VLUrVsXkyZNwooVK4rk1GOGhoawtbVFREQEAMDc3BwikUjHURV+arUacrkcUqkUBgYZ5w4FQUBKSgoiIiJga2tbJMcnEREREREREeW9wOgULryeQ4/ex6FFxeK6DiOdoNgU3Hsby6nkPkNsigKXXkTAxdYUtUrawca8YN4YLVWkVrxwOjnKKzlKvmzfvh3t27fHunXrcOrUKTRv3hxubm4ZJh9EIhFmz5792YEWRM7OzgCgScBQ3hMEARKJBGZmZp9Mdtna2mo+IyIiIiIiIiKi3PYmMknXIeit0HgpwhOkcLI21XUoAFIvxPsExiIwmpVMuSU0TorT8aEo42CBam62MDMuODdISxUqXHwegXgJEy+Ud3KUfJk7dy5EIhEEQYC/vz/8/f0zbVuYky8ikQguLi4oXrw4FAr+ouYHhUKBa9euoWnTph+dTkwsFrPihYiIiIiIiIjyTEyyHLG8Y/6zPHwfh7Zf6P7G2bdRyfAJjIVMWXSXWMgrggD4RyYjMCYFlV2sUdHZCkaGOVoJI9dI5CpcfBGOBAmnlKO8lePKF/qXoaEhL/TnE0NDQyiVSpiamnItFyIiIiIiIiLSGVa9fL7oJDnex6SgZDFznRw/Ra7EnYAYhMTpx/ok+kypEvA4KB6vIhJRzc0WZRwsdLKEQ4pciYvPI5DItXwoH+Qo+TJw4MDcjoOIiIiIiIiIiEgvqNQC3nJ6qlzxKCgOxSyMAQACUqec//fnfxoJgIB/tguAZrMgfPDzv20+/EfQ/Cx88DOQKFXANygeChUX7clPErkaf7+JgV9YImq628HZJv+mnUuWKXHxRQSSmHihfJKj5AsREREREREREVFRFRwrgZxTVOWKBIkSvz8M0XUYlM9iUxS49CICLramqFXSDjbmeTvDTZJMiYvPw5EsU+XpcYg+9FnJF6VSiVOnTuHu3buIiopCvXr1MHjwYABASEgIoqKiULlyZRgZMcdDRERERERERESFg38Upxwjyg2hcVKcjg9FGQcLVHOzhZlx7i/tkChNTfQw8UL5LcerG129ehVlypRBjx494O3tja1bt+LGjRua1y9evIiaNWvi999/z9H+g4OD0b9/f9jb28Pc3Bw1atSAj4+P5nVBEDB37ly4urrCzMwMzZs3x9OnT7X2IZPJMHbsWDg4OMDCwgJff/01goKCcvaGiYiIiIiIiIioyEuRKxEWzzVCiHKLIAD+kck4+TgET4LjoVTlXlVZglSBi8+ZeCHdyFHyxdfXFx06dEBERATGjx+PQ4cOaeZjTNOjRw+Ym5vjyJEj2d5/bGwsGjVqBLFYjDNnzuDZs2dYuXIlbG1tNW2WLVuGVatWYcOGDbh79y6cnZ3RunVrJCYmatp4eXnh2LFjOHDgAG7cuIGkpCR06tQJKhV/2YiIiIiIiIiIKPveRCZD4DIhRLlOqRLwOCgeJx+HwD8yKd315uyKlyhw8Xk4UuS8Fky6kaP5wObPnw+ZTIZz587hq6++yrCNubk5KlWqhAcPHmR7/0uXLkXJkiWxfft2zbZSpUppfhYEAWvWrMGsWbPQvXt3AMDOnTvh5OSEffv2YcSIEYiPj8e2bduwe/dutGrVCgCwZ88elCxZEhcuXEDbtm2zHRcRERERERERERVtb6OTdR0CUaEmkavx95sY+IUloqa7HZxtTLO9j/gUBS6+CIdUwbWZSHdylHy5evUq6tevn2niJY27uzvOnz+f7f2fOHECbdu2Rc+ePXH16lWUKFECP/zwA4YNGwYACAgIQFhYGNq0aaPpY2JigmbNmuHmzZsYMWIEfHx8oFAotNq4urqiSpUquHnzZobJF5lMBplMpnmekJAAAFAoFFAoFNl+H5T70j4Hfh5U0HGskr7hmCV9wbFK+ojjlvQFxyrpI12MW4lUDqhZ+kI5oFZp/0sfFZukwqVnoXC2MUF1N1tYm4mz1C9eIsfVl1GQKZl4+VxqlRIA/zb4r6yejxwlXxISElCiRIlPtpPJZDma4uvNmzfYuHEjJk6ciJkzZ+LOnTsYN24cTExMMGDAAISFhQEAnJyctPo5OTkhMDAQABAWFgZjY2PY2dmla5PW/7+8vb0xb968dNvPnTsHc3PzbL8Pyjs5SeoR6QLHKukbjlnSFxyrpI84bklfcKySPsrPcWv8z4Mopyyinn66EWkkRgA3XmWvjxFyeOGbtAREpP7Lvw20paSkZKldjsagi4sLnj9//sl2T548gYeHR7b3r1arUadOHSxevBgAULNmTTx9+hQbN27EgAEDNO1EIpFWP0EQ0m37r4+1mTFjBiZOnKh5npCQgJIlS6JNmzawtrbO9vug3KdQKHD+/Hm0bt0aYnHWst1EusCxSvqGY5b0Bccq6SOOW9IXHKukj3Qxbo8/CIZClfeVL4IAxEYZwsZOBUNeRS4c1CpYRD1FssMXgIGhrqPRS0aGIlR0toJncUsYGWovZx6bIsc1vyjIWfGSa6q4WiDg4S3+bfAfaTNmfUqOvrrbtGmDrVu34tixY+jWrVuGbXbs2IHAwEBMnjw52/t3cXFB5cqVtbZVqlQJR44cAQA4OzsDSK1ucXFx0bSJiIjQVMM4OztDLpcjNjZWq/olIiICDRs2zPC4JiYmMDExSbddLBZzcBUw/ExIX3Cskr7hmCV9wbFK+ojjlvQFxyrpo3wdtwaGqZmRPLZvnS1O77OG2ESNkmUUcPdUwKO8HB6ecriXU8DMglOf6S0DQyZfckgpAE9Ck+EfLUE1N1uUcbCASCRCVJIMV1/FQKEW8dzmIoN/Mr/820BbVs9FjpIvM2fOxIEDB9CnTx9MmTIFXbp0AZBabvPkyRMcP34cixcvhr29vVYlSVY1atQIL1++1Nrm5+enqaIpXbo0nJ2dcf78edSsWRMAIJfLcfXqVSxduhQAULt2bYjFYpw/fx69evUCAISGhuLJkydYtmxZTt42ERERERERERFRnvv7khlO70udhUUhM8Cb5yZ481z7hmEnNwU8yitSkzGecniUV6CYowqfmBSGqFCQyNX4+00M/MISUa64JR6+j8uXijSi7MhR8sXDwwN//PEHevbsiUWLFmHx4sUQiUQ4dOgQDh06BEEQ4OjoiGPHjmmqVLJjwoQJaNiwIRYvXoxevXrhzp072Lx5MzZv3gwgdboxLy8vLF68GJ6envD09MTixYthbm6Ovn37AgBsbGwwZMgQTJo0Cfb29ihWrBgmT56MqlWrolWrVjl520RERERERERERHkq7J0RNi+0BwB07JeAr7om4d0rMQJfGSPQzxiBr8SIiTBCeJAY4UFi3Ln07zrFljYqeHxQIeNRXgEXDwWMOG0ZFVKxKQrcfRur6zCIMpTjr97GjRvDz88P27Ztw4ULF/D27VuoVCq4ubmhVatWGDFiBGxtbXO077p16+LYsWOYMWMG5s+fj9KlS2PNmjXo16+fps3UqVMhkUjwww8/IDY2FvXq1cO5c+dgZWWlabN69WoYGRmhV69ekEgkaNmyJXbs2AFDQ5aeERERERERERFRwSKXirB2lgOkKQaoUEOK3qPiYGgEOJdU4suvJJp2iXEGCHwl/icZk5qQCXkrRlK8IZ7eM8TTe6aatmJjAW5l5KnTlv2TkHEvJ4e5JasEiIjy0mflva2srODl5QUvL69cCudfnTp1QqdOnTJ9XSQSYe7cuZg7d26mbUxNTbF+/XqsX78+1+MjIiIiIiIiIiLKTbtW2+HdK2NY26kwZn40DDO5cmdlq0aVujJUqSvTbJPLgOAA7QqZQD9jSFMMEPDCBAEvtKctK15C8UGVTOq/xYpz2jIiotzCokMiIiIiIiIiIiIdu37GHJd/t4RIJGD0vCgUK67KVn9jE6B0RQVKV1QASAYAqNVAVKihVoVM4CtjRIcZISJYjIhgMe5e+XfaMgvr1GnL2vZORJ2mkkyOREREWZGj5MurV69w69YtNGnSBKVLl9Zsv3PnDsaPHw9fX194eHhg8eLF6NKlS64FS0RERERERESUXRKJBOHh4QgLC4NEIkHDhg1hYmLy6Y5E+STojRjblxYDAHQbEo8qX8o+0SNrDAyA4iVUKF5Cgrot/k2mJMUbIPC1GO8+qJAJDhAjOcEQz3wM8fy+CQZPi8FXXZNzJQ4ioqIoR8mXlStXYuvWrQgICNBsi4yMRJs2bZCQkACRSITnz5+jZ8+euHv3LqpXr55rARMRERERERERKRQKREREICwsTJNYSXt8+Dw8PBzx8fFafWvWrIkTJ07Azc1NR9ET/UuaIsLamQ6QSQ1Qpa4E3b5PyPNjWtqo8UVtGb6o/W+SRyFPnbbswlErXP7dEtuW2EMmMUD7Pol5Hg8RUWGUo+TLjRs3UK1aNZQsWVKz7ddff0VCQgImTZqERYsW4cyZM+jevTtWrlyJXbt25VrARERERERERFQ4qdVqREVFfTSZkvY8KioqW/s2MTGBs7Mz4uLi8ODBA9StWxe///47vvzyyzx6N0SfJgjAtiXFEPJWDDtHJX6YFw0DQ93EIjYGSlVQYMj0GFhYqXFqjzX2rLWDJEWEboMTuBYMEVE25Sj5EhoaiqZNm2ptO3PmDExMTDBnzhwYGxujS5cuqF+/Pm7fvp0rgRIRERERERFR4fHnn3/i4MGDCA0N1SRXIiIioFJlfZ0LQ0NDODk5wdnZWfNv2uO/z62trSESifD27Vt07twZT548QbNmzbB9+3Z8++23efhOiTJ36bglbp6zgIGhgLELomFTTK3rkCASAd+OjoOZhRqHfrHFkS22kKYYoM+YOCZgiIiyIUfJF6lUClNTU81zlUqFe/fuoX79+rC0tNRsL1WqFB4+fPjZQRIRERERERFR4RAREQEvLy/s378/0zaOjo6fTKY4OTnB3t4eBgYG2Tp+qVKlcPPmTfTt2xenTp1Cnz598Pz5c8yZMyfb+yL6HAEvxNi1yg4A0HtUHCrUyJ11XnKDSAR0/T4BJmYC9qyxwx97rSFNEWHQlFjw14SIKGtylHwpWbIkXrx4oXl+/fp1pKSkoEWLFlrtJBIJLCwsPi9CIiIiIiIiItJ7giBg9+7dmDBhAmJiYmBgYIARI0agbt26WskVR0dHiMXiPI3FysoKx48fx4wZM7B8+XLMnz8fL168wPbt22Fubp6nxyYCgJQkEdbNcoBSIUKtxino0LdgrqvS/ttEmJqpsW1JMVw8ZgWZ1ADDZ0XDMEdXFImIipYcfVW2bNkSmzZtwtq1a9GiRQv8+OOPEIlE6NKli1Y7X19frXVhiIiIiIiIiKjoCQgIwIgRI3D+/HkAQPXq1bF161bUqVNHZzEZGhpi2bJlqFixIkaOHInffvsNb968we+//w5XV1edxUWFnyAAvyywR0SwGI4uSoz8KbpAV5O06JIMEzMBG+fZ48YZC8gkIoyeHwWxsa4jIyIq2HL01T5jxgwUK1YMEydORM2aNXHz5k306tUL1atX17R5+vQp/P390ahRo1wLloiIiIiIiIj0h1KpxKpVq1ClShWcP38eJiYm8Pb2xt27d3WaePnQ4MGDceHCBdjb2+PevXuoW7cufHx8dB0WFWJnD1rh3lVzGIkFjF0UBQtrQdchfVLDNikY7x0FI7GAu1fMsWqqI2RSLgBDRPQxOUq+uLm54eHDh5g9ezZGjhyJrVu3Yu/evVptHjx4gC5duqBXr165EigRERERERER6Y9Hjx6hQYMGmDRpElJSUtC8eXP4+vpi+vTpeT6tWHY1bdoUd+7cQeXKlRESEoImTZrg8OHDug6LCqFXvsbYv94WANBvfCzKVpbrNqBsqNNUgskrImFiqsbj22ZYNsERKclMwBARZSbHRY0lSpTA3Llz8b///Q+DBw9Otyhd//79cezYMTRu3PizgyQiIiIiIiIi/SCRSDBz5kzUrl0b9+7dg42NDbZu3YpLly7B09NT1+FlqkyZMrh16xbat28PiUSCnj17YuHChRCEgl+VQPohMc4A62Y5QKUSoV7LZLTukaTrkLKtaj0ppq2NgJmFGi8emMJ7bHEkxRfgOdOIiHQoT74do6KioFKp8mLXRERERERERFRAXblyBdWrV4e3tzdUKhW++eYbPH/+HEOGDIFIVPDvkLe2tsbJkyfh5eUFAJg9ezb69esHiUSi28BI76nVwMZ59oiJMIKLuwLDZsZAD34lMlShuhyz/hcOSxsV3jwzwcIfiiM+mgkYIqL/ytE347179zB//nw8e/ZMa/uJEyfg4uICJycn2NvbY8OGDbkSJBEREREREREVXLGxsRg2bBhatGiBV69ewdXVFceOHcOhQ4fg4uKi6/CyxdDQEKtXr8Yvv/wCIyMj7N+/Hy1atEBYWJiuQyM9dnKXNR7dMoPYRI1xi6JgZqHfFVWlKyowe2MEbO1VeO9vjPkjnRAdbqjrsIiICpQcJV/Wr1+PRYsWoXjx4pptgYGB6NWrF8LDw+Hs7IykpCSMHz8e169fz7VgiYiIiIiIiKhgOXLkCCpXroytW7cCAEaOHIlnz56ha9euug3sMw0fPhznzp1DsWLF8Pfff6Nu3bp48OCBrsMiPfTMxwSHNtsAAAZNjoW7p0LHEeUOtzIKzN4UDntnJcLeizF/hBPC3hvpOiwiogIjR8mX27dvo0aNGnBwcNBs27ZtG+RyOVauXIng4GDcvXtXc7cIERERERERERUuwcHB6NatG7755huEhYWhQoUKuHbtGjZu3AgbGxtdh5crWrRogb///hsVK1ZEUFAQGjdujOPHj+s6LNIjcdEG2DDbAYJahKYdk9C8c7KuQ8pVziWVmPNLOJxLKhAVZoQFI50Q9Eas67CIiAqEHCVfwsPD4e7urrXt3LlzsLS0xOjRowEANWvWROPGjfHw4cPPDpKIiIiIqDCRy+UICAjAlStXsGvXLixcuBDDhw9Hu3btUKlSJdjY2KB79+6Ij4/XdahEROmo1Wr88ssvqFy5Mo4fPw4jIyP8+OOPePjwIZo0aaLr8HJduXLlcOvWLbRp0wYpKSno1q0blixZAkHQ72mjKO+pVcCG2Q6IjzGEW1k5Bk2J1XVIecLeSYXZm8JRsqwccdGGWDCqOAJeMAFDRJSjWkCVSqX1XCaT4eHDh2jRogWMjY01211dXXH79u3Pi5CIiIiISI8IgoDY2Fi8e/dO8wgMDNR6Hhoa+smLdseOHcPr169x+vRpuLm55VP0REQf9/LlSwwbNkwzxfiXX36JrVu3omrVqjqOLG/Z2trijz/+wIQJE7BhwwbMmDEDz549w+bNm2Fqaqrr8KiAOrLVBs/vm8LUXI3xi6NgYlp4E3a29mr8+HMElk5wxJtnJlg02glTVkWgQnW5rkMjItKZHCVfPDw84Ovrq3l+4cIFyOVytGzZUqtdQkJCoSk1JiIiIiICAIVCgeDg4AyTKmmPpKSkT+7HxMQE7u7umoeHh4fmZ0EQMGDAAPj6+qJ+/fo4ffo0qlWrlg/vjogoY3K5HMuXL8f8+fMhl8thYWGBRYsWYcyYMTA0LBqLbBsZGWH9+vWoXLkyxo4di927d+P169c4duwYnJycdB0eFTCPbpni+PbUa2JDZ8TA1UOp44jynqWNGjPWR2DlZEe8eGCKpeOLY+KySFT5Uqbr0IiIdCJHyZevv/4aS5cuxYQJE9CiRQvMmDEDBgYG6NKli1a7Bw8ewMPDI1cCJSIiIiLKby9fvsTu3bvh7++vSbSEhIRkaaoZR0fHdEmVDxMtjo6OEIlEmfa/desW2rdvj+fPn6NJkyY4evRoupudiIjyw507dzB06FDNTZjt2rXDxo0bUapUKd0GpiOjRo2Cp6cnevbsiVu3buHLL7/EyZMnmSQnjehwQ/w81x4A0KpHIhq0TtFxRPnH3ELA1NWRWDPdAY9vm2H5pOIYtygKtZtKdB0aEVG+y1HyZfLkyTh06BDWrl2LdevWQRAETJw4EZ6enpo2f//9N4KDg9GnT59cC5aIiIiIKD/4+PjA29sbR48ezTDRYmxsnC6h8mGipWTJkjAzM/usGDw8PPDXX3+hW7duuHr1Ktq1a4dff/0V33333Wftl4goq5KSkjB79mysXbsWgiDA3t4ea9euRd++fT+aPC4KWrVqhdu3b6Nz58549eoVGjVqhH379qFz5866Do10TKkE1v/ogKR4Q5SuKEP/8YVznZePMTEVMHFZJDbMdsC9q+ZYM8MBo+ZEo2GbopOEIiICcph8KVasGB4+fIjDhw8jIiICtWvXxldffaXVJiwsDOPHj0f//v1zJVAiIiIiorwkCAKuXLkCb29vnD9/XrO9U6dOaNGihVaSpXjx4jAwMMjzmOzs7PDnn39i0KBBOHDgAAYMGIB3795h5syZRf7CJxHlrbNnz2LkyJEIDAwEAPTv3x+rVq2Co6OjjiMrOCpUqIDbt2+jZ8+euHTpErp06YKlS5di8uTJ/I4uwg78zxavfE1gbqnGuMVREBt/uk9hJDYGxi2Kwi8L7fHXWQv8PMceMokILbok6zo0IqJ8k6PkCwBYWFhg4MCBmb7epUuXdNOQEREREREVNGq1GidOnIC3tzfu3LkDADA0NESfPn0wbdo0VKlSRafxmZiYYO/evXB3d8eyZcvw448/IjAwED///DOMjHL85zwRUYbi4+MxcOBA7N+/H0BqFd4vv/yCtm3b6jiygqlYsWI4e/Ysxo4di19++QVTp07Fs2fPsGnTJpiYmOg6PMpnd6+Y4cx+awDAiJ+iUdxVpeOIdMvQCBj5UzRMzdS4eMwKW73tIZUYoP23iboOjYgoX/D/1oiIiIioSFIoFNi/fz+WLl2KZ8+eAQBMTU0xePBgTJ48GaVLl9ZxhP8yMDDA0qVL4e7ujnHjxmHLli0ICgrCb7/9BktLS12HR0SFgCAI2Lt3L8aNG4fExESIRCKMHz8eCxYs4PfMJ4jFYmzcuBFffPEFvLy8sGPHDvj7++PIkSOsFCpCwoOMsHlh6jovHfsloA7XOAEAGBgA30+Nham5gD/2WmPPGjtIU0To+n0CWCCWdxLjDPD4tike/506Da5nFRk8q8lQsowCBoY6Dk7PSSUi+D81hv8zE1jbqlCjkQS29mpdh0UF1GcnX54+fYpXr14hMTEx04VHBwwY8LmHISIiIiLKFSkpKdi2bRtWrFiBd+/eAQCsra0xevRojB8/Hk5OTjqOMHOjR4+Gm5sb+vTpgzNnzqBZs2b4448/4OzsrOvQiEhPCYKAixcvYvHixbh8+TIAoEqVKti6dSvq1aun4+j0h0gkwtixY1G+fHn06tUL169fx5dffomTJ0/qvIKS8p5cBqyb5YCUJAN4VpWh16g4XYdUoIhEQJ8xcTA1V+PIFlsc3mwLaYoBvh0dxwRMLhEEINBPjAd/meHhTTP4PzWGIPx7cm+csQAAmJqrUa6KDOWryuFZTYZyX8hgbpnx9VxKFRtpCL/HxvB7bAK/xyZ462cMtUp74JapLEPNRhLUbCRBqQoKjmvSyHHy5cKFC/jhhx/g7++faRtBECASiZh8ISLKYwkJCYiKioJSqYRSqYRCociXn93c3DBmzBjY29vr+hQQEX1SXFwc/ve//2Ht2rWIjIwEABQvXhwTJkzAqFGjYGNjo+MIs6ZLly64fPkyOnXqhPv376NBgwY4c+YMKlasqOvQiEiPKBQKHDx4ECtWrMCjR48ApE5z+M0332Dz5s0wNzfXcYT6qW3btrh9+zY6deqEN2/eoGHDhjhw4AA6dOig69AoD+1ZY4e3L41hZavC2EVR4Kyg6YlEQPchCTA1E7B3nR1O7bGGVCLCwEmxyIdl9AolSbIIT+6Y4uFNMzy8ZYq4KO2B5+4pR42GEhgZAX6+xnj9xATSFAM8uWOGJ3dSK2JEIgFuZRTwrPZPQqaqDE5uyiKbPFCrgaA3Yk2ixe+xCSJD0v9CFyuuRLkqckSFGuLNcxO8eZb6OLLFFnaOyn8SMVJ8UVcKE1Mmt4qyHP3n4N69e+jYsSNEIhH69u0LX19f+Pr6Yvr06fD398eFCxcQGxuL77//Hu7u7rkdMxERAZBIJDh58iT27NmDM2fOQKlU6iSO1atXY+bMmRg7dizMzMx0EgMR0ceEhoZizZo12LhxIxITU+cYL1WqFKZOnYpBgwbp5XdXvXr1cOvWLbRv3x6vX79Gw4YN8fvvv6NJkya6Do2ICrj4+Hhs3rwZa9euRXBwMADA3NwcQ4YMwZgxY/D8+XOIxWIdR6nfKlWqhDt37qBHjx64evUqOnfujJ9++gkTJ06ElZWVrsOjXPbXn+a4eMwKIpGAH+ZFw7540V7n5VM69E2Eqbkavy4thgtHrCCTiDBsZgwMmbD6JEEAQgONUpMtN83w4qEJVMp/syQmZmpUqStFjYYSVG8oTTcW1ap/Egu+Jnjla4JXvsYIDxLjvb8x3vsb49Kx1HbWdip4VpXBs2pqQqZ0RTmMC2kCQSZNnUIsLdHy6okJUhK1s4EikYCS5RQoX02GCtVlKF9NBgfnf89tbJQBHt00w/0bZnhyxxSxkUa4dNwKl45bQWyixhd1/qmKaSzh90MRlKOvNm9vbyiVSpw9exatW7fG999/D19fXyxatAhA6l2FI0aMwKlTp3Dv3r1cDZiIqChTq9W4fv06du/ejUOHDiEhIUHzmoWFBcRiMYyMjGBkZKT183+f50Y7Q0NDHD58GI8fP8a0adOwYcMGLFq0CP369YMBb10iogLgzZs3WLZsGXbs2AGZTAYgdSqd6dOno3fv3nq/WH25cuVw8+ZNfP3117h9+zZat26N3bt3o2fPnroOjYgKoHfv3mHt2rXYsmWLJhHt7OyMcePGYcSIEShWrBgUCgWeP3+u40gLB3t7e5w7dw6jR4/G1q1bMXfuXKxZswZjxozBuHHjuBZMIREcYIRtS4oBALp+n4Bq9aQ6jkg/fNU1GSamAjYtsMf105aQSgwwZn4UjD6R9xUEQKUEFHIRlEoRlHIRFApAqUj7WQRl2nPFP8/l/277tx+g/Cdp4eSmhIu7Ai7uSpiaF7wEg1wqwvMHJnj4V2p1S0Sw9klyLqlAjUYS1GggRcWaUoiNM9+XgSHg7qmAu6cCrbonAQDiow3w6sk/iQdfYwS8MEFCrCF8rpnD51pqBaShkYBSFeQoX1WmqZCxc9TPJEJctAH8Hv1b1fL2pTFU/5lCzMRMjXJfyFG+mgzlq8tQrooM5haZjw07BzWaf52M5l8nQy4Dnt83xYO/UpMx0WFGqZ/dX2bYvgzwKC9HzUYS1GosQelKclZ9FQE5+j/OmzdvombNmmjdunWGr9va2mLXrl0oVaoUfvzxR+zcufOzgiQiKuqeP3+O3bt3Y+/evZr1CQDA3d0d/fv3R//+/VGpUqV8j2vmzJnYs2cPfvzxR7x//x4DBgzAqlWrsHz5crRq1Srf4yEiAoDHjx9jyZIlOHjwINTq1MUvGzZsiBkzZqBDhw6FKkHs6OiIixcvol+/fjh+/Dh69eqFlStXYsKECRAV1fkiiEjL/fv3sXLlShw8eBAqVerFssqVK2Py5Mno27cvTExMdBxh4WVsbIzNmzejWbNmWLBgAfz8/LBw4UKsXLkSw4YNw6RJkzhbiB6TSkRYN8sBMokBvqgjRfch8boOSa80apcCY1MBG2Y74O5lc0zv5wIzC7V20iQt0aL495GX7ByVcHH/Jxnjkfqvq4cCDs6qfF2kPjLUEI/+qW55es8Ectm/f7saiQVUrClFzUZS1GgggbP7582AYWOvRp1mEtRpJgEAKOTA25fG/yRjUhMU8TGG8H9qAv+nJjhzILWfo4sytTqmmgzlq8pQsqyiwFUvqdVAyFux1not4UHpM3x2jsrUREs1OSpUl8K9XM7fi7EJUL2BFNUbSDFwUize+6euw/PghhlePzFGoF/q4/h2G1jbqTQVMVXqSmH2kQQP6a8cDaWYmBg0a9ZM89zYODWtmpycDAuL1AWcTExM0KRJE5w/fz4XwiQiKnrCw8Oxf/9+7NmzBz4+Pprt1tbW6NmzJ7777js0adJEpxcRDQ0NMXDgQPTq1Qtr166Ft7c3Hj58iNatW6Nt27ZYtmwZqlWrprP4iKhouXHjBpYsWYI//vhDs61du3aYMWMGmjRpUmiTEebm5jh8+DAmTJiA9evXY9KkSQgMDMSqVatgaJiPVwqIqMAQBAFnz57FihUrcOnSJc32r776CpMnT0a7du0K7XdiQSMSidC/f3/06dMHx48fh7e3N3x8fLBu3Tr8/PPP6NevH6ZNm6aTG6ko5wQB2LHcDkFvjGHroMToeVH5enG+sKjbXIJJKyKxeqoDQt9lf7pDA0MBYrEAQzEgFgsw+uchNhZgZAQYGX+wTZyauDAyFmBklNpGqRAh7L0Rwt6LkRBriNhII8RGGuGZj6nWcYzEApzcUqtjXDxS/3X1UMDZXQkrG/VnnwelEnj12OSf6cRMEfRGu3wlbQ2R6g2lqFJHmqcVOmJjwLOqHJ5V5QASIQipyaBXj00005W9ey1GZKgRIkONcPPcP9eB/6kWSZuuzL2cAkZiAQYGqZ+TgQG0fhYZINfXlZFLRfB//sEUYr7GSE7Q/sVMW+NGawoxF1WerHEjEgHu5RRwL6dAl4EJSIg1wKNbZnjwlyke3TJDQqwhrp6yxNVTljASC6hUS6qpinF01c/KIkovR8kXR0dHralu0spl37x5g6pVq2q2SyQSxMcz809ElFUpKSk4fvw49uzZg3PnzmnuTjQyMkL79u3x3XffoXPnzjA1Nf3EnvKXmZkZpk+fjqFDh2LhwoX4+eef8eeff+LcuXMYOHAgFixYADc3N12HSUSFkCAIOHPmDLy9vXHjxg0AgIGBAXr27Inp06ejRo0aug0wnxgaGmLt2rXw8PDA5MmTsW7dOrx//x579+7VyzVtiChnZDIZ9u3bhxUrVuDZs2cAUr8fevfujUmTJqFWrVo6jrDoMjQ0RI8ePdC9e3dcuHAB3t7euHz5Mnbu3Ildu3aha9eumDFjBurWravrUCkLLp+wwPXTlhAZCBizIBo29p9/Ab6oqlZPiuUHQxHw3PifxMi/SROxJlmCfxIqqc/TEim5mfBKijdA6HsjhAaKEfrOCKHvUv8Nfy+GQi5CcIAxggPSz+llaaPSJGNc3FMTMi4eCjiVUH50CrD4GAM8+tsCD2+awvdvM6Qk/XtTpchAQPmqMlRvKEXNhhKULKfIk+RAVohEQHFXFYq7pqBRuxQAgCRZBP9nxprKmNdPTJCSZICn90zx9F7Wr1WIDDJPznwsaZPRdqVChPf+Yq01cADA2ESNsv9MIVbhnynELKx0U2FibadGkw7JaNIhGUoF8OKhiaYqJjxIDN+/zeD7txl2rQLcysg1VTGeVeRM7uqxHCVfypUrB39/f83zL7/8EoIg4JdffsGGDRsAAK9fv8alS5dQpkyZ3ImUiKiQUqlUuHLlCnbv3o0jR44gKSlJ81q9evXw3XffoVevXnoxL7SDgwPWrFmDsWPHYubMmfjtt9+wY8cOHDhwABMmTMC0adNgY2Oj6zCJqBBQKpU4cuQIlixZgkePHgFIrcYeOHAgpk6dinLlyuk4wvwnEokwadIkuLm5YcCAATh27BhatmyJEydOwMHBQdfhEVEeiomJwS+//IJ169YhLCwMAGBlZYXhw4dj3LhxnNqqABGJRGjdujVat26Nv//+G0uWLMHx48dx7Ngxzff29OnT0bJlS1YnFVCPHgHbltsCAHqNjEelmjLdBlQIODir4OAs0WkMljZqeNrI4VlFrrVdrQKiwg21kzL//BwTYYSkeEO88jXEK1/tKRxFBgKKu/47jZmzuxIOzkr4PxHD93pTvHpl95/jq1C9QWrlQ9UvpbDMhYqavGJmIaBKXRmq1E0d+2o1EBwgxivfj0/v9V+CWgSVGukSJp/D1l4Fz2oyVPhnvRaP8nIUxGUejcTQnMP+4+MQGmikScS8fGyCoDfGCHpjjJO7bbTGRvUGEphbcnoyfZKj4dehQwdMnz4dT548QZUqVdCuXTt4eHhg48aNuHv3Ltzc3HDp0iVIpVIMGTIkt2MmIioUfH19sXv3buzbtw/BwcGa7aVLl9as41K+fHkdRphzZcuWxcGDBzFx4kRMmTIF169fh7e3N7Zs2YKffvoJI0aM0ExZSUSUHVKpFH/++ScmTZqkuRnI0tISI0eOxIQJE+Dq6qrjCHWvd+/ecHFxQZcuXXDr1i00bNgQZ86cQdmyZXUdGhHlsjdv3mDNmjXYtm0bUlJS70guUaIEvLy8MGzYMN70UsDVq1cPx44dw7Nnz7B06VLs3bsXFy9exMWLF1GnTh3MmDEDXbt2LVRrlem7hASgZ09AITNAjUYSdOqf8OlOpNcMDNMqP1So3kD7NalEhLAPqmQ+TNBIUwwQHiRGeJAYD29mXIVcqoIcNRpKUKORBGUr6W91g4EBULKsAiXLKvBV12TNdrX6n4cKUKtFUKsBIe3ntH+F1NcFNaBWif7pk/Z61n8GAPeycji65s0UYnlJJAJcSynhWioRHfslIjlBhEe3UxMxj26bIineEH+dtcBfZy0gNhZQv1UyWnZPQrkv5Hr3XouiHCVfBgwYABsbG81dGMbGxjhx4gR69eqFu3fv4u7duzAwMMDQoUMxfvz4XA2YiEifhYSEYN++fdizZ4/mTm0AsLW1Re/evfHdd9+hYcOGheYut3r16uHq1as4efIkpk2bhhcvXmDcuHFYt24dvL290aNHj0LzXokob6nVamzevBnz5s3T3NVtb2+P8ePHY/To0ShWrJiOIyxYmjZtips3b6J9+/Z49eoVGjRogFOnTuHLL7/UdWhElAvu3LmDFStW4MiRI1CrU++Orl69OiZPnoxevXrxJhc9U7lyZezcuRPz58/HypUrsXXrVty7dw89evRAhQoVMG3aNPTr14+fawGQkABYWQEOzkqM/CkazIsVbaZmAkpVUKBUBYXWdkEA4qIN/knGiBEamJqQiQw1hKuHAvWq+KFia3vYOeko8HySNjVY6tXnD6s1WLnxMRbWAhq2SUHDNilQKQE/XxM8uJGajAkJFOP6aUtcP22JUuXlaNUjEQ3apMDUjOe0oBIJgpCrn86LFy8QGxuLcuXK6cUUOR+TkJAAGxsbxMfHw9raWtfhEACFQoHTp0+jQ4cOEIuzvxgbUX75cKzKZDIcPXoUe/bswcWLFzX/gywWi9GpUyf0798fHTt2hImJySf2qt+USiW2bduGOXPmIDw8HABQv359LF++HI0bN9ZxdMTvVyrIfH19MXz4cNy+fRtA6hSHM2fOxPDhw2FhYaHj6Aq20NBQdOrUCffv34eZmRkOHDiAr7/+WtdhFTn8jqXcoFarcerUKaxYsQLXr1/XbG/bti0mT56cK9NUcawWDBEREVi3bh02bNigWUe3ZMmSmDRpEoYOHcr/9v1Hfo9bqRTYfDoUDiUUn25M9F9qFSwiHiO5eDXobakL6YQgAG+eGeP8EUvcvmABhTz1v/nmlqlrybTqngjXUspcP261Epbw97nOvw3+I6t5g2zl6J88eYLFixdj1KhR8PLywq+//orExEStNhUrVkSDBg30PvFCRPQ5lEolHjx4gIEDB8LJyQkDBw7E+fPnoVar0bBhQ2zcuBFhYWE4evQounfvXugTLwBgZGSEESNG4NWrV5gzZw7Mzc1x+/ZtNGnSBN26dcPLly91HSIRFTApKSmYPn06atWqhdu3b8PKygqrV6/Gxo0bMWbMGF58ygIXFxdcvXoV7dq1g0QiQbdu3fDzzz/rOiwiygaJRILNmzejUqVK6NKlC65fvw6xWIyBAwfi0aNHOHv2LFq1asVq4kKkePHiWLhwId69e4elS5fC2dkZ79+/h5eXFzw8PLBgwQLExMToOswiy9QUcHHP/QucREQfIxIBZb+QY+RPMVh/Mhh9x8bCyU2BlCQD/PmbFaZ864rFY4rj70tmUPIrqsDIcvJl5syZqFGjBmbPno1ffvkF69evx7Bhw1CuXDnNXYhEREVdSEgIJk2ahDJlymDevHnYv38/UlJSUK5cOcybNw+vX7/GX3/9hZEjRxbZKXKsrKwwd+5cvH79GsOHD4eBgQGOHz+OL774Aj/88IOmKoaIirY///wTVapUwdKlS6FUKtGjRw88f/4co0eP5h1X2WRpaYkTJ05gyJAhUKvVGD16NKZPn66pxCSigikqKgrz58+Hh4cHRowYAT8/P9jY2GD69Ol4+/YtduzYgWrVquk6TMpD1tbWmDp1KgICArBp0yaUKVMG0dHR+Omnn+Dh4YHJkycjJCRE12ESEVE+s7JRo2O/RKz4LRTT1kSgdtMUiAwEPL1ninUzHTG+qyuObLFBTASrq3QtS8mXo0ePYsmSJVCr1WjevDkmTpyIoUOHwsPDA5GRkejRowekUmlex0pEVGDJZDIsXboU5cuXx6pVqxAWFgYrKyuMHDkSt27dgp+fH3766ScudvwBFxcX/PLLL/D19UXnzp2hUqmwceNGlCtXDgsWLEBycvKnd0JEhU54eDj69u2Ldu3aISAgACVLlsSJEydw+PBhlChRQtfh6S2xWIwtW7ZgwYIFAIClS5eif//+kMlkOo6MiABALpfj/v372Lx5M4YPH47atWvDxcUFc+bMQWRkJDw8PLBmzRq8f/8e3t7ecHV11XXIlI9MTU0xYsQIvHz5Evv27UO1atWQlJSElStXonTp0hg+fDhev36t6zCJiCifGRgA1epLMXFZFNYcDUGXQfGwKaZCXJQRjm6zwfhurlgz3QFP7pogdxceoawyykqjX375BSKRCNu3b8eAAQM022UyGXr06IEzZ87g2LFj6NOnT54FSkRUEAmCgFOnTmHixIma/+GpX78+pkyZArVajS5duvAO7U+oXLkyTpw4gatXr2LKlCm4e/cufvrpJ2zcuBHz58/HoEGDYGSUpf9cEZEeU6vV2LZtG6ZOnYq4uDgYGBhg/PjxmD9/PiwtLXUdXqEgEonw448/wt3dHUOGDMH+/fsRGhqKY8eOwdbWVtfhERUZCoUCz549w7179zSPx48fQy6Xp2tbu3ZtTJkyBT169ODfQwQjIyP06dMH3377Lc6cOQNvb2/cuHEDW7ZswbZt2/DNN99g+vTpqFmzpq5DJSKifObgrEKvkfHoPiQed6+Y48JRS7x4YIq7V8xx94o5XDwUaNktCU07JMHCmpmY/JKlypf79++jZs2aWokXADAxMYG3tzcEQcD9+/fzJEBvb2+IRCJ4eXlptgmCgLlz58LV1RVmZmZo3rw5nj59qtVPJpNh7NixcHBwgIWFBb7++msEBQXlSYxEVDS9ePECHTp0wNdff43Xr1/D2dkZO3fuxF9//YXOnTsz6ZJNzZo1w99//40DBw6gdOnSCA0NxbBhw1CjRg388ccfEHibBlGh9ezZMzRr1gzDhw9HXFwcatWqhTt37mDVqlVMvOSBAQMG4PTp07CyssKVK1fQqFEjvHv3TtdhERVKSqUST548wY4dOzBmzBjUr18fVlZWqFGjBoYOHYpNmzbh3r17kMvlsLOzQ6tWrTB9+nQcPnwYAQEBuHv3Lnr37s3EC2kRiUTo0KEDrl+/juvXr6Njx45Qq9X47bffUKtWLbRv3x7Xrl3j389EREWQkRho0DoFszdGYMneULT+JhGm5mqEBoqxZ40dxnQugS2LiiHgBa9Z5Ycs/QUXGxuLdu3aZfhapUqVAABxcXG5FlSau3fvYvPmzenmsV22bBlWrVqFHTt2oHz58li4cCFat26Nly9fwsrKCgDg5eWFkydP4sCBA7C3t8ekSZPQqVMn+Pj4wNCQ890RUc7Fx8dj/vz5WLduHZRKJcRiMSZOnIhZs2ZpvoNUKpWOo9RPIpEIvXv3RteuXbFx40YsWLAAT58+RadOndC8eXMsX74cderU0XWYRJRLJBIJFi1ahGXLlkGhUMDCwgILFy7EmDFjeKExj7Vu3RrXr19Hhw4d8OzZM9SvXx+nT59GjRo1dB0a0UcplUrs3bsXL168gKOjI5ycnFC8eHHNw9HRUWffHyqVCi9fvoSPj4+mouXBgweQSCTp2lpbW6NOnTqoXbs26tSpgzp16qB06dIQiUQ6iJz0WePGjXHq1Ck8fvwYS5YswcGDB3H27FmcPXsW9evXx8iRI9GzZ0+Ym5vrOtRcp1QqceHChQyrxoiICChZVoFBk2PRe1Qc/vrTAheOWOK9vzGunLTElZOWKFtZhlY9klC/ZQqMTZmwzwtZ+qtUrVZnegd32h+2uX2hMSkpCf369cOWLVuwcOFCzXZBELBmzRrMmjUL3bt3BwDs3LkTTk5O2LdvH0aMGIH4+Hhs27YNu3fvRqtWrQAAe/bsQcmSJXHhwgW0bds2V2MloqJBrVZjx44dmDFjBiIiIgAAnTp1wqpVq+Dp6anj6AoXExMTeHl5YdCgQfD29sbatWtx5coV1K1bF126dMGsWbNQt25dXYdJRJ/hwoULGDVqlGbKxq+//hobNmxAyZIldRxZ0VG9enXcvn0bHTp0wJMnT1C/fn306NED33//Pb766isYGGSpSJ4oXwiCgGPHjmHGjBnw8/P7aFt7e3sUL148XWLmw+dpP1taWuYo4aFWq/Hq1Svcu3dPk2y5f/9+hmvWWVpaapIsaf+WLVuWv2OUq6pVq4Z9+/ZhwYIFWL58ObZv347bt2/j9u3bGDduHPr27Ythw4ahVq1aug71swiCgAcPHmD37t3Yv38/wsPDUa1aNcyfP1/XoRERFVhmFgJadU9Cy25J8HtsjAtHrPD3JXP4PzOB/zMT7F1ri6adktGyWxKcSyp1HW6hUmBvKRw9ejQ6duyIVq1aaSVfAgICEBYWhjZt2mi2mZiYoFmzZrh58yZGjBgBHx8fKBQKrTaurq6oUqUKbt68mWnyRSaTaS06mpCQACB1Tl6FQpHbb5FyIO1z4OdB+e327duYMGECfHx8AADly5fHypUrNd8n/x2THKu5I+0u+OHDh2POnDnYt28ffv/9d/z++++aaTmaNGnCu0RzAccs5ZfIyEhMmTIF+/btA5D6N9qaNWvQpUsXiESiT45BjtXc5ezsjEuXLqFfv344f/489u3bh3379sHd3R39+/fHgAEDUKZMGV2Hqfc4bj/PtWvXMHPmTNy5cwcA4ODggB49eiAuLg6RkZGIiIhAREQEoqKioFarER0djejoaDx//vyT+zY1NU1XOZPRz8bGxnj06BEePHgAHx8f3L9/H4mJien2Z25ujpo1a6J27dqoVasWatWqhfLly6dLtKhUqgJZKc2xqv/c3d2xfv16zJo1Czt27MD27dsREBCATZs2YdOmTahRowYGDx6Mb7/9Vq/W+3r37h3279+vqXxL4+DggIYNG+b/dRu1ClDzLnHKAbVK+1+ifCQCUKGqBBWqStB/nAGunrLCxeNWiAoT4/Q+a5zeZ41q9VLQslsiajZMgaERoFalJmP4t4G2rJ4PkZCFSUANDAxgaWkJBweHDF8PDAyEhYVFhq+LRCL4+/tnKZg0Bw4cwKJFi3D37l2YmpqiefPmqFGjBtasWYObN2+iUaNGCA4Ohqurq6bP8OHDERgYiD///BP79u3D999/r5VIAYA2bdqgdOnS+OWXXzI87ty5czFv3rx02/ft21coS3SJ6NNiYmKwa9cuXLlyBQBgZmaG3r17o2PHjlzTRQeCgoJw5MgRXL16FWq1GkDq9Jc9evRA7dq1mYQhKsAEQcClS5ewY8cOJCYmauar79evH//OKgAEQcDr169x8eJFXL9+Xevu/SpVqqBly5Zo0KABTE1NdRglFTVv377F7t27NTe/mJqaokuXLujSpUuG3xsqlQpJSUmIi4tDfHy85vHh8w9/lkqlnxWfsbExSpcujXLlymkerq6unOaaChS1Wg1fX19cuHABt27dglKZehHN2NgYjRo1QqtWrVC5cuUC+Xd0cnIybt68iatXr+LJkyea7WKxGHXr1kXz5s1Rq1YtTlVKRPQZVCrg/n0nnD1bCvfvO0EQUv974OCQgrZtA9GqVSDs7GSf2EvRk5KSgr59+yI+Ph7W1taZtsty8iWnRCJRtu7oef/+PerUqYNz586hevXqAJBh8iUkJAQuLi6afsOGDcP79+9x9uzZTJMvrVu3RtmyZbFp06YMj51R5UvJkiURFRX10ZNI+UehUOD8+fNo3bo1L3xTnpLJZFi3bh0WL16suQA1aNAgLFiwAE5OTp/sz7Gat96+fYuVK1dix44dmu/tGjVqYNq0aejWrRun8cgBjlnKSy9evMCYMWNw7do1AKlTo2zcuDFH0wdyrOY9qVSK33//Hbt27cKFCxc0CzZbWVnhm2++wcCBA9GgQYMCeaGuoOK4zZ53795h3rx52LNnDwRBgJGREYYOHYqZM2fC2dk5146TnJyMiIgIreqZtOfh4eFa25OTk1GlShXUqlVLU9VSqVKlQnfRl2O1cIuOjsbevXvx66+/4tmzZ5rt5cuXx+DBg9G/f38UL15chxGmjsFz585h7969OHXqlFaStGnTpujXrx+6d+8OGxsbrT75PW6PPwiGQsXKF8oBtQoWUU+R7PAFYMBkPRUsEcFGuHjcCldOWSEpPnV8GhkJ6N7dD9u3l+TfBh9ISEiAg4PDJ5MvWfpLMSAgINcC+xQfHx9ERESgdu3amm0qlQrXrl3Dhg0b8PLlSwBAWFiYVvIlIiJCc0HU2dkZcrkcsbGxsLOz02rTsGHDTI9tYmICExOTdNvFYjEHVwHDz4TyiiAIOHXqFCZOnKhZh6B+/fpYt25dji4ScqzmDU9PT2zatAlz5szBypUrsWnTJjx8+BB9+vRBxYoVMWPGDPTp04fnPgc4Zik3SaVSLFmyBN7e3pDL5TA3N8e8efPg5eX12RcsOVbzjlgsRv/+/dG/f3+8f/8eu3btwvbt2+Hv74/t27dj+/btKF++PAYNGoQBAwagRIkSug5Zb3Dcflx0dDS8vb2xYcMGzc0VvXr1wsKFC/NkfT1bW1vY2tqifPnyub5vfcexWjg5Oztj0qRJmDhxIv7++29s3boVBw4cgJ+fH6ZPn44ff/wRXbp0wbBhw9CqVat8q+ISBAF3797F7t27ceDAAURFRWleq1SpEr777jv069cP7u7uH91Pvo5bA0Pg0/cyE2XOwJDJFypwipcU0GdsAnoMT8Dfl8xx8agVXvmaoHjxFP5t8B9ZPRdZujXYw8Pjsx7Z0bJlS/j6+uLhw4eaR506ddCvXz88fPgQZcqUgbOzM86fP6/pI5fLcfXqVU1ipXbt2hCLxVptQkND8eTJk48mX4ioaHvx4gU6dOiAr7/+Gq9fv4azszN27tyJv/76i4u7F1AuLi5YsWIFAgMDMXv2bNja2uLFixcYOHAgypcvj02bNn32lCJElDNXrlxB9erVMW/ePMjlcnTo0AFPnz7F5MmTC92d4oVZyZIlMWvWLLx69QrXrl3D999/DwsLC/j5+WHmzJlwd3dH+/bt8dtvv6WrOifKqpSUFCxZsgRly5bFypUrIZPJ0KJFC9y5cwcHDx7Mk8QLUVEmEolQv359bN26FSEhIdi8eTPq1q0LpVKJI0eOoF27dihTpgzmzZuH9+/f51kcAQEBWLBgASpWrIh69ephw4YNiIqKQvHixeHl5YV79+7h6dOnmDFjxicTL0RElHuMTYAm7VMwd0s4Dp2NR5MmwboOSW8VuHlZrKysUKVKFa2HhYUF7O3tUaVKFYhEInh5eWHx4sU4duwYnjx5gkGDBsHc3Bx9+/YFANjY2GDIkCGYNGkSLl68iAcPHqB///6oWrUqWrVqpeN3SEQFTXx8PCZNmoSqVavi7NmzEIvFmDZtGvz8/DBgwABOYaUH7O3tMX/+fAQGBsLb2xuOjo54+/YtRo0ahTJlymDVqlVa6xcQUd6Jjo7G4MGD0aJFC/j5+cHZ2Rm//fYbTp06hVKlSuk6PMohkUiEJk2a4Ndff0VYWBh+/fVXNGnSBGq1GmfPnkXv3r3h6uqKsWPH4v79+8jCzMZEUCqV2Lp1Kzw9PTFjxgzEx8ejWrVqOHPmDC5evMibX4jygbW1NYYNG4Y7d+7g4cOHGDt2LGxtbfHu3TvMnTsXHh4e6NChA44ePZoriy3HxsZi8+bNaNKkCcqUKYOffvoJfn5+MDMzQ58+fXD69GkEBwdj9erVXNORiKgAqPiFCiYmWV9ShLTp5RXFqVOnwsvLCz/88APq1KmD4OBgnDt3DlZWVpo2q1evRteuXdGrVy80atQI5ubmOHnyJBc/JCINtVqNX3/9FeXLl8eqVaugVCrRqVMnPH36FEuWLNH6TiH9YG1tjenTp+Pt27dYu3Yt3NzcEBoaikmTJsHDwwMLFy5EXFycrsMkKpQEQcCuXbtQsWJFbN++HSKRCKNGjcLz58/Rs2dPXjwpRCwtLfH999/j2rVrePXqFWbNmgU3NzfExMRgw4YNqF27tma9xsjISF2HSwWQIAg4fvw4qlatimHDhiEkJAQeHh7YvXs3Hjx4gHbt2vE7g0gHqlevjnXr1iEkJAR79uxB8+bNIQgCzpw5gx49esDNzU1zk1p2yOVyHD9+HD169ICzszNGjBiBGzduQCQSoWXLltixYwfCw8Oxb98+tG/fnhWyRERUaOhF8uXKlStYs2aN5rlIJMLcuXMRGhoKqVSKq1evokqVKlp9TE1NsX79ekRHRyMlJQUnT55EyZIl8zlyIiqobt26hXr16mHIkCGIiIhAhQoVcObMGZw8eZJTWxQC5ubmGDduHPz9/bFlyxaULVsW0dHRmD17Njw8PDBz5kxeECTKRa9evUKrVq0wcOBAREVFoUqVKvjrr7/w888/w9bWVtfhUR4qV64cFi5ciLdv3+LPP//Et99+CxMTEzx+/BgTJkxAiRIl0L17d5w8eRJKpVLX4VIBcOPGDTRq1AjdunXDixcvYG9vj9WrV+Ply5fo378/K46JCgAzMzP069cPly9fhp+fH6ZNmwYnJydERERg2bJlqFChApo3b449e/ZAIpFkuA9BEHDz5k2MGjUKLi4u6NatG44ePQq5XI6qVati2bJleP/+PS5cuICBAwfyxjciIiqU+JctERUpISEhGDBgABo2bIh79+7BysoKK1aswOPHj9GuXTtdh0e5zNjYGEOHDsWLFy+wd+9efPHFF0hISIC3tzc8PDzg5eX1f/buOzyKqm0D+D3bsiWb3klI6ERAuvQiJQiiIArSi4IiiiIivIj6Iiq8FAGFD2xIR4qCKCgqHQSkBSGhBQglISEkpJet8/0RWVhSCJtNNpvcv+vaK9kzZ2ae3TwZwjx7zkFcXJyjwyRyWnq9Hp988gkaNWqE3bt3Q6lUYtasWTh58iTatGnj6PCoHEmlUkREROD7779HQkIClixZgpYtW8JgMGDLli149tlnERwcjHfffRdnz551dLjkANHR0Xj22WfRoUMHHD58GCqVCtOmTcPly5cxYcIEuLi4ODpEIipEnTp18L///Q83btzAli1b0KtXL0gkEuzbtw/Dhg2zTDn5zz//AAAuXbqE6dOno06dOmjXrh2+/PJL3LlzB4GBgXjnnXdw6tQpnD59Gu+++y6qVavm4FdHRERUtjiWk4iqBJ1Oh4ULF+KTTz5BVlYWAGDUqFGYOXMmAgICHBwdlTWZTIbBgwdj4MCB+Pnnn/Hpp5/i+PHj+Pzzz7FkyRKMHDkSU6ZMQa1atRwdKlUC8fHxWL16NY4fP46AgACEhoYiNDQUYWFhCA0NhZ+fn1NNp5OXl4fr168jNjYWV69exdWrVy3fx8TE4M6dOwCAiIgILF26FDVr1nRwxORonp6eeO211/Daa68hKioKK1aswOrVq3Hr1i3MmzcP8+bNwxNPPIFRo0Zh4MCBHB1VBlJSUhAfH49atWpBo9E4NJYbN27gv//9L1auXAmz2QypVIrRo0fjww8/RFBQkENjI6KSk8vl6Nu3L/r27YsbN25g+fLlWLZsGa5fv47Fixdj8eLFqF69Oq5fv27ZR6PRoF+/fhg2bBi6dOnCaeCJiKjKYfGFqIoSRRFRUVEQBAGenp7w8vKCSqVydFh2J4oitm3bhokTJ+LSpUsAgNatW+OLL77gIq5VkEQiQd++fdGnTx/8+eef+PTTT7F//3588803WLZsGQYNGoSpU6eiQYMGjg6VnExOTg62bNmClStXYufOncUuNq5UKlG9enVLMeb+R1hYGIKCgsr15oRer8eNGzcKLa7ExsYiISGh2P39/PywYMECDBo0yKmKSlQ+GjZsiHnz5mHWrFn47bff8N1332H79u04evQojh49irfffhuDBg3C+PHj0bRpU0eH63REUcTNmzcRGRmJkydPWr7ef/MzLCwMDRo0sHqEh4dDrVaXaWx37tzB//73P3zxxRfQ6XQAgOeffx6ffvop6tWrV6bnJqKyFRISgg8//BDTpk3Drl278M0332Dr1q24fv06JBIJunfvjmHDhqFv374OLwATERE5EosvRFWM0WjExo0bMWfOHMvQ8LuUSiW8vLwsj7tFmQcfD7a7ubmV2Q03s9mMzMxMpKenWz3S0tIKtBX1yMjIAAAEBARg9uzZnE+cIAgCIiIiEBERgYMHD2LmzJn47bffsHbtWqxduxbPPfccpk2bhubNmzs6VKrAzGYzDh48iJUrV2LTpk3IzMy0bOvQoQOeeeYZpKam4tq1a5ZHfHw88vLycPHixSIXq5XJZAgODi4wYubuIyQk5JGm5zEajYiLi7MqqNxfZImPjy+2WATkf3K1Ro0aCAsLQ1hYmNX34eHhlbJ4T/Yll8vx7LPP4tlnn0VSUhLWrFmD5cuXIyoqCsuXL8fy5cvRvn17jB8/Hs899xzkcrmjQ65wRFHElStXChRakpKSCu3v4eGBtLQ0y+/79u3bLdsEQUCNGjUKFGXq169f6t/n3NxcLFq0CLNmzUJaWhoAoGPHjpg9ezZat25dqmMTUcVyd8rJiIgIJCUl4ciRI2jZsiUCAwMdHRoREVGFYFPxZd26dXjhhRegUCjsHQ8RlZGcnBx89913+Oyzz3D16lUA+QspajQapKamwmQyIS8vDzdv3sTNmzcf6dhSqRSenp6FFmsebANQZJGksIJKZmbmQ28KPoxCocDbb7+NadOmcSFHKqB9+/b49ddfcfLkScycORObN2/Gli1bsGXLFvTo0QPTpk1Dhw4dHB0mVSBXrlzBqlWrsGrVKsTGxlraa9SogeHDh2PYsGFFTmGn1+sRFxdnVZC5evWq5fsbN27AYDBYbpYWRhAEBAYGFhg1ExwcjNTU1ALFlbi4OJhMpmJfk0qlshRT7i+u3P3q7e3NUS1kN35+fpg4cSLefvttHDlyBIsWLcKmTZtw8OBBHDx4ENWqVcNrr72GMWPGwM/Pz9HhOoTRaMSFCxesCi2RkZFIT08v0FcikSA8PBzNmjVDs2bN0LRpUzRp0gTu7u5ITk5GdHR0gUdycjKuXLmCK1eu4JdffrE6Vs2aNQsUZerVqwelUllszCaTCStXrsSHH36I+Ph4APkjn2bPno2ePXvyGkJUyfn5+eHZZ591dBhEREQVik3Fl6FDh+LNN9/EsGHD8PLLL6Nhw4b2jouI7CQ5ORn/93//h0WLFiElJQUA4OvrizfffBPjxo2Dl5cXRFFEZmYm7ty5U+CRmppaaPvdbbm5uTCZTEhOTkZycnKZvQ6FQgF3d/eHPjw8PAq0BQYGsuhCD9WsWTP88MMPOHv2LP73v/9h3bp1+P333/H777+jWbNmGD58OAYNGlRlbwRWdRkZGdi0aRNWrlyJAwcOWNq1Wi369++PESNGoH379g8dVadQKFCzZs0i10UxmUxITEy0Ksg8WKTJzc21FMoPHz5covgVCkWho1bufu9s69BQ5SAIAtq0aYM2bdpg3rx5+Oqrr/DVV18hPj4e77//PmbMmIGBAwdi/PjxaNGihaPDLTM6nQ7R0dE4efKkpdDyzz//IDc3t0BfhUKBRo0aWRVaGjVqVOQUYj4+PujUqRM6depk1Z6UlFRoUebOnTu4dOkSLl26hK1bt1r6SyQS1K5du0BRpm7dulAoFPjll18wdepUnD17FgBQvXp1fPzxxxgyZAjXeCAiIiKiKksQbfhI+ahRo/DDDz8gOzsbgiCgdevWGDNmDAYMGFDmcweXp4yMDLi7uyM9PR1ubm6ODocAGAwG/Prrr+jVqxeno3iIq1evYv78+Vi2bBlycnIA5H8qe9KkSRg1apTdpojJzc0tUKAprmAD4KHFksIeSqXSqW4MMled35UrVzBnzhwsX74cer0eQP4or6eeegrDhw/HM888U6mmWiqLnDWZTMjNzUVeXp7V17vf6/V61KlTB6GhoRXy99tkMmHnzp1YtWoVtmzZYrkRKggCunXrhhEjRuC5554r1799RFFEcnJyoaNm4uLi4OXlVWiRJSAgoNJMt8jra+Wm0+nwww8/YNGiRfj7778t7a1bt8b48eOddvT93bzt2LEjzp49azWiJSoqCkajscA+Go0GTZo0sSq0PPbYY2WW96Io4tatW4UWZe5OH/YgqVQKf39/y6hpLy8vTJs2DePGjXvoSBmqmHiNJWfkiLzddPwGDKbSzdBAVZTZBE3SaWT7PQ5I+AEFqvger+aKyycO8G+DB5S0bmBT8QUAMjMzsXbtWnz77bc4efIkBEGAVqvF4MGDMXr0aDRr1szm4CsKFl8qHv5n4OH++ecfzJkzBxs2bLBMM9O0aVNMmTIFzz//PGQyLvVUHpirlUdycjI2bNiAVatW4ejRo5Z2Nzc3DBgwAMOHD0e7du2c+sZ2fHw8jh8/joMHD6J+/fowGo1WRZIHvy+srbDvDQZDic7v4+ODFi1aoGXLlpavjpwr/OzZs1i5ciXWrFljNQ1jeHg4RowYgaFDh6JatWoOi6+q4/W16jh69CgWLVqEDRs2WK4nAQEBGDt2LF599VUEBAQ4OMLi6fV6REZG4tChQ/j777/x119/FbnGkpeXF5o2bWpVaKldu3aFGDUiiiISEhIKLcrcXVdPqVRiwoQJmDJlCjw8PBwbMJUKr7HkjFh8IafC4gs5GRZfClfmxZf7nTp1Cl9//TW+//57pKenQxAENG7cGK+++ioGDx7stNP9sPhS8fA/A4UTRRH79u3D7NmzsWPHDkt7t27dMGXKFHTt2rVCfqq8MmOuVk7nz5/HmjVrsHr1aly/ft3SHhYWhmHDhmHYsGGoU6eOAyMsmTt37mDPnj3YtWsXdu3aVeTC7/akUCigVCqhUqmgUqmgVCohkUgQExNTaJEmKCjIqhjTokULeHt7l1l8KSkp+P7777Fy5UocP37c0u7l5YVBgwZhxIgRaNGiBa+lFQCvr1XPrVu38PXXX2Pp0qVISEgAAMjlcvTv3x9vvvkmWrVq5eAI892+fRuHDx/GoUOHcOjQIRw7dgx5eXkF+gUFBRUotFSvXt3pri+iKCI+Ph4xMTEIDw+v8MUwKhleY8kZsfhCToXFF3IyLL4UrlyLL3fl5eVh48aN+Oabb/DXX39BEASoVCq8+OKLGDduHJo3b26vU5ULFl8qHv5nwJrJZMJPP/2E2bNn49ixYwDy5+Tu378/Jk+eXClGoDkr5mrlZjabsX//fqxevRqbNm1CZmamZVvr1q0xfPhwvPjii/Dy8nJglPdkZ2fjwIED2LVrF3bv3o3IyEirT15LJBI0aNAAZrMZ1apVg1qtthRI7i+WFPd9cduVSmWRn97W6XQ4ffo0jh07huPHj+PYsWM4e/YszGZzgb41atSwFGNatmyJZs2alerf57u/pytXrsS2bdssRSCZTIZevXphxIgRePrpp+Hi4mLzOcj+eH2tuvR6PTZv3oxFixbh0KFDlvaWLVti/PjxGDBgQLn9vprNZpw7d85SaDl06FChhWxvb2+0bdsWLVq0gMlkwpgxYxAcHFwuMRLZgtdYckYsvpBTYfGFnAyLL4Urad3ArvMPmUwm6PV66HQ6APmfhtLpdFi+fDlWrFiBfv36YdmyZSxkEJVSXl4eVq1ahXnz5iEmJgZA/nQPL730EiZOnIhatWo5OEKiyk0ikaBz587o3LkzFi1ahK1bt2L16tX4/fffceTIERw5cgRvvfUWevfujeHDh6NXr17lukaBXq/H0aNHLSNbjhw5UmB0yWOPPYauXbuia9eu6NSpEzQajUNutri4uFiKKXdlZ2cjMjLSUow5duwYYmJiEBsbi9jYWGzatAlA/tor9erVsxod06RJk2LXYBFFEZGRkVi5ciXWrVuH5ORky7amTZtixIgRGDx4MHx9fcvuRRORTRQKBQYOHIiBAwfixIkTWLRoEb7//nscO3YMw4cPx6RJk/DKK69g7Nixdp8aMCsrC0ePHrUUWg4fPlzoOijh4eFo164d2rZti7Zt26Ju3boQBMFyY9Df39+ucRERERERUcVll+LL0aNH8c0332DDhg3Izs6GRCJBnz598Oqrr6JTp07YsmUL5syZg82bN0OtVmPlypX2OC1RlZOWloalS5fi888/x61btwAAnp6eeP311zF+/Hj4+fk5OEKiqketVmPQoEEYNGgQEhMT8f3332PVqlU4deoUtmzZgi1btsDLywsDBw7EsGHD0KpVK7tPLWM2m/HPP/9Yii0HDhxAdna2VZ/Q0FBLsaVLly4Fpocp6fos5UGj0aB9+/Zo3769pS0tLQ0nTpywFGSOHz+Oa9eu4fz585bp4ID8xZ8bNGhgNWVZo0aNkJKSgrVr12LlypWIioqyHDcgIABDhw7F8OHD0ahRo3J/rURkm+bNm2PFihWYO3cuvvnmGyxZsgTx8fH45JNP8L///Q/9+vXDm2++ibZt2z7yNVcURdy4cQN//fWXpdjyzz//WNbSu0utVuOJJ56wFFtat25dYUY8EhERERGR49lcfElLS8Pq1avx7bffIioqCqIoIjg4GJMmTcLo0aMRFBRk6Tt48GAMGDAATZs2xfbt2+0SOFFZMxqNuHPnDlQqlcPXLYqPj8fChQvx1VdfWaY3CgkJwcSJEzF69Gi4uro6ND4iyhcQEIC3334bb7/9Ns6cOYPVq1djzZo1SEhIwJIlS7BkyRLUqVMHw4cPx9ChQxEWFmbTeURRRExMjGUasT179iAlJcWqj6+vL7p06YIuXbqga9euqFmzptOtJ3A/Dw8PS/HorqSkJBw/ftxqhMytW7dw+vRpnD59GsuWLQOQ/2l5o9FomcrMxcUFffv2xYgRI9C9e3fIZHYdCExE5cjX1xfvvfce3n33Xfz0009YtGgRDhw4gI0bN2Ljxo1o2rQpxo8fj0GDBkGpVBZ6DIPBgFOnTlkVW+Lj4wv0CwkJsYxoadeuHR5//HFOvUBEREREREWy6W7DsGHD8OOPP0Kn00EQBDz11FN49dVX0bt3b0gkksJPJJOhZcuWHPVCDqHT6ZCSkoLk5GSkpKRYfV9UW3p6umV/Hx8f1KhRAzVr1izwNSQkpMz+433u3DnMnTsXa9assXwqvWHDhpg8eTIGDhzI//ATVWCNGjXCnDlzMGvWLOzatQurV6/G5s2bERMTgw8++AAffPABOnbsiOHDh+OFF16Au7t7sce7efOmZWTLrl27EBcXZ7Xd1dUVnTp1shQoGjZsWOS/yZWFn58fevXqhV69egG4t/jz/cWY48ePIzU1FQDQtm1bjBgxAgMGDICHh4cDIycie5PL5ejfvz/69++PU6dOYfHixVi7di0iIyPx0ksv4d1338Urr7yC1157DWq1GocPH7YUW44dO4bc3Fyr40mlUjRt2tRSbGnbti1CQkIc9OqIiIiIiMgZ2VR8Wbt2LQICAvDSSy9hzJgxCA0NLdF+zz33XIn7EhVGFEXk5eXh+vXryMjIeGgB5W5bVlZWqc6bnJyM5ORky6L295NKpQgJCSmyOOPr6/vInzY/dOgQ5syZg61bt1raOnbsiMmTJ6NXr15O/el1oqpGKpUiIiICERERWLJkCbZs2YJVq1Zh9+7d2L9/P/bv34833ngDffr0wbBhwxAREQG5XI47d+5g7969ltEt58+ftzquQqFA27ZtLdOItWzZssoXZAVBQHBwMIKDg9G3b18A+f9uxMbGQiaToXr16o4NkIjKRZMmTfDtt99i9uzZ+Pbbb7FkyRJcv34ds2bNwuzZsy2j4O7n6elpVWhp2bIlNBqNA6InIiIiIqLKwqbiy8aNG9G3b99HnqbjmWeewTPPPGPLKamKysrKws6dO7F9+3bs2rULCQkJyMvLs+lYEokE3t7e8Pb2ho+Pj9XXoto8PT2RnZ1tWeT5ypUrVl+vXr2KvLw8XL16FVevXsWePXsKnFetVhdZmKlRo4blP/Zmsxm//vorZs+ejYMHDwLIv5HYt29fTJ48Ga1bt7b9jSSiCkGr1WL48OEYPnw44uLisHbtWqxatQpnz57Fhg0bsGHDBvj5+SE4OBiRkZEQRdGyryAIaN68uWVkS7t27YpdWJ7yCYKAmjVrOjoMInIAb29vTJkyBe+88w5++eUXfPHFF9i7dy8AoF69epa1Wtq2bYt69epV+tGCRERERJWRm0qG2n6uiL2djdScirOWKRFgY/HlhRdesHccRBZXrlzBtm3bsH37duzduxd6vb5AH7lc/tDCyYNt7u7uNv2n2t3dHU2aNEGTJk0KbDObzUhMTLQqyNz/fVxcHHJychAdHY3o6OhCj+/n54caNWogPT3d8sl2hUKBYcOG4d1330W9evUeOWYiqviCg4MxZcoUTJ48GSdPnsTq1auxbt06JCUlISkpCQAQHh5uKbZ06tQJnp6eDo6aiMj5yGQyPPfcc3juuecQFxcHpVIJHx8fR4dFRERERKXgqpShUTV3hHmrIQgC6ge44WpyNk7HpyMrz+jo8IgA2Fh8IbIng8GAv/76C9u3b8e2bdsKTK1Ts2ZNPP300+jVqxdq1qyJkydPol+/flAoFA6K+B6JRIKgoCAEBQWhXbt2BbbrdDpcv3690FEzV65cQWpqqtWNVjc3N4wdOxZvvfUWgoKCyvvlEJED3B3R0rx5c8ydOxe7du1CWloaOnbsyOsAEZGdBQcHOzoEIiIiIioFjYsUDYLcUdNHA4nEelr+MB8NqnupEZOUheib6cgzFJxulqg8laj4UprpOgRBwOXLl23enyqn5ORk/Pbbb9i2bRt+//13q8XtpVIpOnTogKeffhq9e/dGvXr1LGucGAwGnDt3zmnWPHFxcUGdOnVQp06dQrenp6dbCjE5OTl45plnHrroNhFVXnK5HE899ZSjwyAiIiIioipIIgAqhRQahQwGkxlZOiMMJvHhOzoRlUICjUIGV6UMeQYTEtN1jg6JSkitkKJBkBtq+boWKLrcTyIRUC9Ai5q+GpxPyMS5xAwYK1kek/MoUfHl6tWrZRwGVXaiKOL06dOW6cSOHDlitZaBj48Pevbsid69eyMiIgIeHh6OC7YcFTelGRERERERERGRvQhC/g1sjUIGjYsMri4yaFyk/36VQa2QFviwq85oQrbOhKw8I7J0RmTr//3678NUwQYWyKQCXC2vLf+rq1IGV0X+a5VJ701HL4oiDl9OwdWUHAdGTA+jlEvQIMgdtf1cIS2m6PIguVSCRsHuqOPviuib6Yi5lQUzazBUzkpUfDGbK9iVlJxCTk4Odu3ahe3bt2P79u2Ii4uz2t64cWP07t0bTz/9NJ544glIpVIHRUpERERERERE5NwEAVDJpdA8UFRx/bewolHIih0xUBgXmRQuMim8NIVP/Z6rNyFTZ0C2zoRsnRGZef8WZvRG5OhNEO18s/tuAen+AotWee91KuUlv7ckCALa1PKGVCLg8u1s+wZKpeYikyA80A11/V2timaPSimXonmoF+r6a3EmLp3FNipXXPOF7OratWuWtVv27NmDvLw8yzaVSoVu3bpZ1m8JCQlxYKRERERERERERM7FMm3Wv4UHq0KLDcWV0scjhUohBbQFt5nNIrL1xvyRM7p7I2bufi1qPQ4XmcR61Mp9o3Ts/RoFQUCrmvkFmIu3sux2XLKdXCogPNAN9QK0kJei6PIgrVKOtrV9UD9Qj39upCEhPe/hOxGVEosvVCpGoxFHjhyxTCcWFRVltb169ero3bs3evfujc6dO0OlUjkoUiIiIiIiIiKiiu3uyA43lRxuSjnclPkFCPW/BZdHmXbJ0SQSAVqlHFqlvNDtRpM5vzCjN8JsFi3FFnvecC+pFmFekEgEnE/ILPdzUz6ZVED9AC3qB7hBISu7HPDSKPBkfT/cyshD5PU03MnWl9m5iEpdfLl+/ToSEhKg0xW9QFXHjh1LexqqQO7cuYMdO3Zg+/bt+O2335CammrZJpFI0LZtW8t0Yg0aNCgwXygRERERERERUVUmkwhwU8nyCyx3Cy2q/AJLaaZYciYyqQTuagnc1YUXZ8pbs+qekEkERMVnODqUKkUmEVA3QIv6AdpHmjautPzdlHiqYQCup+Tgn7g0ZOYZy+3cVHXYXHz57rvv8PHHH+P69esP7WsymWw9DVVA3bp1Q2RkpOW5p6cnnnrqKfTu3Rs9evSAt7e3A6MjIiIiIiIiIqoY8kex3CuyaJX532tcOBlNRfR4sAckgoDTcemODqXSk0qA2n5aNAhyK9eiy4Oqe6sR7KnCleQsnIlPR66ea5+T/dh0pV++fDlGjx4NAGjUqBHq1q0LV1dXuwZGFVfPnj2h1+sto1vatGkDmYx/NBARERERERFR1SOV5K8ncXf0yv2FFkdMoUWl07CaO2RSASevpTk6lEpJIgC1/VzRIMg9f72gCkAiEVDbT4swbw3OJ2biXEIGDCbR0WFRJWDTHfP58+dDJpPhxx9/xDPPPGPvmKiCmzFjBj799FNHh0FEREREREREVO5q+rpC4yKFmzK/wOLqIuOU65VM/QA3SAUBx66mPrwzlYhEAGr4aNCwmnuFHfklk0rQsJo7avu54mxCBmJuZcLEgTBUCjZlekxMDDp27MjCSxUllVaMqjQRERERERERUXlrHurp6BCoHNTx10IiEXA09g5EDoKwmSAAYd4aNKzmBq2yYqzv8zBKuRTNqnuinr8Wp+PScTUlmzlANrGp+OLl5cVpxoiIiIiIiIiIiKjSquXrCqkg4MiVFJh58/2RCAIQ6qVGg2rucFc5R9HlQRoXGdrU8kZ4oBanbqThZlqeo0MiJ2NT8aVPnz7YunUrcnNzoVKp7B0TERERERERERERkcOF+WgglQj461IyCzAlFOKlwuPVPOCuds6iy4M81Ap0rueHpMw8nLqehuQsvaNDIidh06pfM2fOhJubG0aOHIm0tDQ7h0RERERERERERERUMYR4qdGhri+kNt1JrTrUCik61fNFhzq+labwcj8/rRIRDQLQoY6P047mofJVopEvL730UoG28PBw/PDDD/jjjz/QokULBAcHF7q4mCAIWLZsWekjJSIiIiIiIiIiInKAah4qdKrrh/0Xb8PIITAF1PLVoGl1Tyhklb9CFeKlRrCnCtdScnA2IQNpOQZHh0QVVImKLytWrChyW3p6Onbt2lXkdhZfiIiIiIiIiIiIyNkFuCvRub4v9l64DaOJBRgA0LhI8UQNLwS6V62lKQRBQJiPBmE+Gty4k4Pomxm4k83pyMhaiYove/bsKes4iIiIiIiIiIiIiCo0P60SXer7Yc/5JBiqeAGmjr8rmoR4QF7F52ML8VIjxEuNhPRcRMVn4HamztEhUQVRouJLp06dyjoOIiIiIiIiIiIiogrPx9UFXcP9sed8EnRGs6PDKXcaFyla1/SGv5vS0aFUKIHuKgS6q5CUmYfo+AwkpOc5OiRyMJvKkqtWrcKhQ4ce2u/IkSNYtWqVLacgIiIiIiIiIiIiqpC8NAp0C/eHUl61Rn3UC3DF040CWXgphp9WiSfr+6FHA38Ee1at6djImk1Xh5EjR+Lbb799aL9ly5Zh1KhRtpyCiIiIiIiIiIiIqMJyV8vRNdwfaoXU0aGUOa1Shm6P+aF5qBdkVXyasZLydnVBx7q+6NUoAGHeagiCoyOi8lamvylmsxkCs4qIiIiIiIiIiIgqIXeVHF3D/aBxqZwFGEEA6gdq0bNhAPy0HO1iCw+1Am1r+6D344Go6auBhLfLq4wyLb5cuXIFbm5uj7zfrFmz0LJlS2i1Wvj5+aFv3764cOGCVR9RFDF9+nQEBQVBpVKhc+fOiI6Otuqj0+kwfvx4+Pj4QKPR4Nlnn0VcXFypXhMRERERERERERHRXVqlHN3C/aFVlmh5bafhppKh+2P+aFbdk6Nd7ECrlKN1TW880zgIdf1dwbe08ivxFWHGjBlWz0+dOlWg7S6j0YgLFy5g//796N69+yMHtW/fPrz++uto2bIljEYjpk2bhoiICJw9exYajQYAMGfOHMyfPx8rVqxA3bp18cknn6B79+64cOECtFotAGDChAn45ZdfsH79enh7e+Odd95B7969ceLECUillbMaTUREREREREREROVL4yJDt3B/7Dp/Cxm5RkeHUyqCAIQHuqFRNXdIOUzD7jQuMrQI80KDIHecS8zApaQsGE2io8OiMlDi4sv06dMhCAJEUYQgCDh16hROnTpV7D5+fn6YOXPmIwe1Y8cOq+fLly+Hn58fTpw4gY4dO0IURSxcuBDTpk1Dv379AAArV66Ev78/1q1bh1dffRXp6elYtmwZVq9ejW7dugEA1qxZg5CQEOzcuRM9evR45LiIiIiIiIiIiIiICqNSSNEt3B97zichNcfg6HBs4qGWo1UNL3i7ujg6lEpPpZCiWXVPNAhyw4XETFxIzISBRZhKpcTFl+XLlwPIn+7rpZdeQvv27fHyyy8X2lehUCAoKAitW7eGi0vpf1HT09MBAF5eXgCA2NhYJCYmIiIiwtLHxcUFnTp1wqFDh/Dqq6/ixIkTMBgMVn2CgoLQsGFDHDp0qNDii06ng06nszzPyMgAABgMBhgMznnBrGzu/hz486CKjrlKzoY5S86CuUrOiHlLzoK5Ss6IeUsVjRRAx9qe2B+TjNTsB/LSbLL+WoFI/l3bJTzADRKJwN+pciQBEO6vQS1vFa4kZ+FiYhZ0RrOjwwIAmE35o7iYD9ZK+n4Ioig+cjntySefRM+ePTF58uRHDuxRiaKIPn36IDU1FQcOHAAAHDp0CO3atUN8fDyCgoIsfV955RVcu3YNv//+O9atW4dRo0ZZFVMAICIiAjVq1MBXX31V4FzTp0/HRx99VKB93bp1UKvVdn5lRERERERERERERETkTHJycjB48GCkp6cXu+a9TatA7dmzx+bAHtUbb7yB06dP4+DBgwW2CYL1nIN3p0QrTnF9pk6diokTJ1qeZ2RkICQkBBEREcW+iVR+DAYD/vzzT3Tv3h1yudzR4RAViblKzoY5S86CuUrOiHlLzoK5Ss6IeUsVmdFkxl+XkpGUqc9vMJugSY5Gtk8DQOL49aglAvBYkBvq+Wsh4douFY7ZLOJqSjbO38pCdp5j1hFqGKRB7KnDvMY+4O6MWQ9jU/GlvIwfPx4///wz9u/fj+DgYEt7QEAAACAxMRGBgYGW9qSkJPj7+1v66PV6pKamwtPT06pP27ZtCz2fi4tLodOkyeVyJlcFw58JOQvmKjkb5iw5C+YqOSPmLTkL5io5I+YtVURyOdA5PBAHYpKRkJ53b4NE6vDii5dGgdY1veChVjg0DipevSAF6gZ64GpKDiKvpyLPUL7TkUmk+eUDXmOtlfS9kNh6grNnz2LkyJGoWbMmVCoVpFJpoQ+Z7NHrO6Io4o033sDmzZuxe/du1KhRw2p7jRo1EBAQgD///NPSptfrsW/fPkthpXnz5pDL5VZ9EhISEBUVVWTxhYiIiIiIiIiIiMheZFIJOtX1RXUvNWQVYHSJVAI0DnFHjwb+LLw4CUEQUMNHg+ahng/vTBWKTSNfDh8+jG7duiE3NxcA4O3tDVdXV7sF9frrr2PdunXYunUrtFotEhMTAQDu7u5QqVQQBAETJkzAzJkzUadOHdSpUwczZ86EWq3G4MGDLX1ffvllvPPOO/D29oaXlxcmTZqERo0aoVu3bnaLlYiIiIiIiIiIiKgoEomA9nV8YDC449df/0H3x/ygFyXI0RmRozchW29Ert6EbL0JuXojTGU0uMHHVYFWNb3hruIIBmcU6q3BpaQs3MrQPbwzVQg2FV+mTp2K3NxcTJgwAe+//z68vLzsGtTSpUsBAJ07d7ZqX758OUaOHAkAmDx5MnJzczFu3DikpqaiVatW+OOPP6DVai39FyxYAJlMhgEDBiA3Nxddu3bFihUrIJU6fk5FIiIiIiIiIiIiqno81Ipipy3KM5j+LcbcK8rklKJAI5MIaBTsjvoB2oeul00VW4tQL/wWlQCz6OhIqCRsKr4cP34cTZo0wfz58+0dD4D8acceRhAETJ8+HdOnTy+yj1KpxKJFi7Bo0SI7RkdERERERERERERUNpRyKZRyKTw1RU8LlmcwIeeBokxhBRpfrQta1fSCm5KjXSoDd7UcdQO0OJ+Q6ehQqARsKr4oFArUrl3b3rEQERERERERERER0UPcLdB4PaRAo5RzBqDKplE1d1xLyUauvozmpyO7kdiyU/v27XHmzBl7x0JEREREREREREREdsDCS+Ukl0rQNMTT0WFQCdhUfJk5cyZu3LiBzz77zN7xEBERERERERERERFREcJ8NPB3c3F0GPQQNk07dvLkSYwaNQqTJ0/GL7/8gu7duyM4OLjIBZuGDx9eqiCJiIiIiIiIiIiIiChfi1Av/BaVAPPDl08nB7Gp+DJy5EgIggBRFLF//34cOHCg0H6iKEIQBBZfiIiIiIiIiIiIiIjsxF0tR90ALc4nZDo6FCqCTcWXDz/8sMhRLkREREREREREREREVLYaVXPHtZRs5OrNjg6FCmFT8WX69Ol2DoOIiIiIiIiIiIiIiEpKLpWgaYgnDl1OcXQoVAiJowMgIiIiIiIiIiIiIqJHF+ajgZ/WxdFhUCFsGvlyv4SEBBw6dAg3b96EIAgIDAxE27ZtERgYaI/4iIiIiIiIiIiIiIioCC3CPLEjKhFm0dGR0P1sLr7cvn0b48ePx48//giz2XpOOYlEgueffx6LFi2Cr69vqYMkIiIiIiIiIiIiIqKCPNQK1PHX4kJipqNDofvYVHxJT09Hx44dceHCBahUKkRERCAsLAwAcO3aNfzxxx/YuHEj/vnnHxw5cgTu7u72jJmIiIiIiIiIiIiIiP7VqJo7rt/JRq7e/PDOVC5sKr7873//w4ULF9C/f38sXry4wOiW5ORkvPHGG9i4cSNmz56NmTNn2iVYIiIiIiIiIiIiIiKyppBJ0CTEE4cvpzg6FPqXxJadtmzZgpCQEKxZs6bQacV8fHywevVqhISE4Mcffyx1kEREREREREREREREVLQaPhr4al0cHQb9y6biy7Vr19CuXTvI5fIi+8jlcrRr1w7Xr1+3OTgiIiIiIiIiIiIiIiqZlmGeEARHR0GAjcUXlUqF5OTkh/ZLTk6GSqWy5RRERERERERERERERPQIPNQK1PV3dXQYBBuLL82bN8e+fftw4sSJIvucOHECe/fuRYsWLWwOjoiIiIiIiIiIiIiISq5RNQ8o5Tbd+ic7sukn8Pbbb8NgMKBr16746KOPEBMTA71eD71ej5iYGEyfPh3dunWDyWTC22+/be+YiYiIiIiIiIiIiIioEAqZBE1CPBwdRpVnU/GlV69e+PTTT5GVlYUZM2agfv36UKvVUKvVqF+/Pj7++GNkZmbik08+Qc+ePe0dMxERERERERERERERFaGmryt8tS6ODqNKs3ns0dSpU3HkyBEMHToUYWFhkMvlkMvlCAsLw7Bhw3D48GFMnTrVnrESEREREREREREREVEJtAj1hCA4OoqqS1aanVu0aIGVK1faKxYiIiIiIiIiIiIiIrIDT40Cdf1dcSExy9GhVElcdYeIiIiIiIiIiIiIqBJqVM0DSjnLAI7Ad52IiIiIiIiIiIiIqBJSyCRoEuLh6DCqpBJNO1azZk2bTyAIAi5fvmzz/kREREREREREREREZJuavq64lJSF5Cy9o0OpUkpUfLl69SoEQYAoio98AoEr+hAREREREREREREROUzLMC/siE6EDbf4yUYlKr7c1bx5cwwdOhR9+vSBSqUqq5iIiIiIiIiIiIiIiMhOPDUK1PFzxcVbWY4Opcoo0Zov69evR+/evXH69GlMnDgRjRs3xtSpUxEVFQU/Pz/4+/sX+yAiIiIiIiIiIiIiIsd5PNgDLjIuA19eSvRODxgwAFu3bkVCQgIWLVqEBg0aYMWKFYiIiEBwcDDeffddnDp1qoxDJSIiIiIiIiIiIiIiWyhkEjSp7uHoMKqMRypzeXl5Ydy4cTh06BCuXLmC6dOnw83NDZ999hmaN2+Ohg0bYvbs2bhx40ZZxUtERERERERERERERDao5esKb1eFo8OoEmweYxQWFoYPPvgA586dw99//43x48cjJSUF7733Hlq2bGnPGImIiIiIiIiIiIiIyA5ahnlBEBwdReVnlwneQkNDUbNmTQQFBUEURZjNZnscloiIiIiIiIiIiIiI7MhLo0BtP1dHh1HpyWzdMScnB5s3b8batWuxa9cumEwmuLu7Y/To0Rg+fLg9YyQiIiIiIiIiIiIiIjt5PNgd11NyoDNyIEVZeaTii9lsxu+//441a9bg559/Rk5ODuRyOXr37o2hQ4eid+/eUCg4XxwRERERERERERERUUXlIpOicYgHjsbecXQolVaJii9///031q5diw0bNuD27dsQBAHt2rXD0KFDMWDAAHh4eJRxmEREREREREREREREZC+1/Vxx+XYWUrL0jg6lUipR8aVNmzYQBAGPPfYY3n77bQwZMgQhISFlHRsREREREREREREREZWRlmFe+D06EaLo6EgqH8mjdL58+TJmzJiBevXqQa1Wl+ih0WjKKnYiIiIiIiIiIiIiIrKRl0aB2n6ujg6jUirxmi+iKCIvL68sYyEiIiIiIiIiIiIionL0eLA7rqfkQGc0OzqUSqVEI1/MZnOpHkREREREREREREREVPG4yKRoHOLh6DAqnUeadoyIiIiIiIiIiIiIiCqX2n6u8HZVODqMSoXFFyIiIiIiIiIiIiKiKq5FqCcEwdFRVB6VvviyZMkS1KhRA0qlEs2bN8eBAwccHRIRERERERERERERUYXi7eqCWr6ujg6j0qjUxZcNGzZgwoQJmDZtGiIjI9GhQwf07NkT169fd3RoREREREREREREREQVSuMQd7jIKnXZoNxU6ndx/vz5ePnllzF69GiEh4dj4cKFCAkJwdKlSx0dGhERERERERERERFRheIik6JxiLujw6gUZI4OoKzo9XqcOHEC//nPf6zaIyIicOjQoUL30el00Ol0lucZGRkAAIPBAIPBUHbBUond/Tnw50EVHXOVnA1zlpwFc5WcEfOWnAVzlZwR85acCfOVnEV1DxfEqCQwm4wAmLMPKun7IYiiKJZxLA5x8+ZNVKtWDX/99Rfatm1raZ85cyZWrlyJCxcuFNhn+vTp+Oijjwq0r1u3Dmq1ukzjJSIiIiIiIiIiIiKiii0nJweDBw9Geno63NzciuxXaUe+3CUIgtVzURQLtN01depUTJw40fI8IyMDISEhiIiIKPZNpPJjMBjw559/onv37pDL5Y4Oh6hIzFVyNsxZchbMVXJGzFtyFsxVckbMW3ImzFdyNlk5Ouzfu4s5+4C7M2Y9TKUtvvj4+EAqlSIxMdGqPSkpCf7+/oXu4+LiAhcXlwLtcrmcyVXB8GdCzoK5Ss6GOUvOgrlKzoh5S86CuUrOiHlLzoT5Ss7C9d/JoJiz1kr6XkjKOA6HUSgUaN68Of7880+r9j///NNqGjIiIiIiIiIiIiIiIiJ7qrQjXwBg4sSJGDZsGFq0aIE2bdrg66+/xvXr1zF27FhHh0ZERERERERERERERJVUpS6+vPjii0hJScGMGTOQkJCAhg0b4tdff0VoaKijQyMiIiIiIiIiIiIiokqqUhdfAGDcuHEYN26co8MgIiIiIiIiIiIiIqIqotKu+UJEREREREREREREROQILL4QERERERERERERERHZEYsvREREREREREREREREdsTiCxERERERERERERERkR2x+EJERERERERERERERGRHMkcHUJGJoggAyMjIcHAkdJfBYEBOTg4yMjIgl8sdHQ5RkZir5GyYs+QsmKvkjJi35CyYq+SMmLfkTJiv5GyYs4W7Wy+4Wz8oCosvxcjMzAQAhISEODgSIiIiIiIiIiIiIiKqKDIzM+Hu7l7kdkF8WHmmCjObzbh58ya0Wi0EQXB0OIT8qmJISAhu3LgBNzc3R4dDVCTmKjkb5iw5C+YqOSPmLTkL5io5I+YtORPmKzkb5mzhRFFEZmYmgoKCIJEUvbILR74UQyKRIDg42NFhUCHc3Nz4C09OgblKzoY5S86CuUrOiHlLzoK5Ss6IeUvOhPlKzoY5W1BxI17uKrosQ0RERERERERERERERI+MxRciIiIiIiIiIiIiIiI7YvGFnIqLiwv++9//wsXFxdGhEBWLuUrOhjlLzoK5Ss6IeUvOgrlKzoh5S86E+UrOhjlbOoIoiqKjgyAiIiIiIiIiIiIiIqosOPKFiIiIiIiIiIiIiIjIjlh8ISIiIiIiIiIiIiIisiMWX4iIiIiIiIiIiIiIiOyIxRciIiIiIiIiIiIiIiI7YvGFSm3//v145plnEBQUBEEQ8NNPP1ltv3XrFkaOHImgoCCo1Wo89dRTiImJserTuXNnCIJg9Rg4cKBl+969ewtsv/s4duxYsfGdOXMGnTp1gkqlQrVq1TBjxgyIomjZnpCQgMGDB6NevXqQSCSYMGFCqd8TqpicPVdHjhxZ6HEbNGhQ+jeHKqzyyFsAuHjxIvr06QMfHx+4ubmhXbt22LNnz0Pj4zWW7nL2XOU1tmoqr7w9efIkunfvDg8PD3h7e+OVV15BVlbWQ+PjNZYA589TXl+rJnvkLQAcPnwYXbp0gUajgYeHBzp37ozc3FzL9tTUVAwbNgzu7u5wd3fHsGHDkJaW9tD4eH2l+zl7vvI6WzWVV95++umnaNu2LdRqNTw8PEocH6+z+Vh8oVLLzs5G48aNsXjx4gLbRFFE3759ceXKFWzduhWRkZEIDQ1Ft27dkJ2dbdV3zJgxSEhIsDy++uory7a2bdtabUtISMDo0aMRFhaGFi1aFBlbRkYGunfvjqCgIBw7dgyLFi3CvHnzMH/+fEsfnU4HX19fTJs2DY0bN7bDO0IVlbPn6ueff2513Bs3bsDLywv9+/e3w7tDFVV55C0APP300zAajdi9ezdOnDiBJk2aoHfv3khMTCwyNl5j6X7Onqu8xlZN5ZG3N2/eRLdu3VC7dm38/fff2LFjB6KjozFy5MhiY+M1lu5y9jzl9bVqskfeHj58GE899RQiIiJw9OhRHDt2DG+88QYkknu3sgYPHoxTp05hx44d2LFjB06dOoVhw4YVGxuvr/QgZ89XXmerpvLKW71ej/79++O1114rcWy8zt5HJLIjAOKWLVsszy9cuCACEKOioixtRqNR9PLyEr/55htLW6dOncS33nqrxOfR6/Win5+fOGPGjGL7LVmyRHR3dxfz8vIsbbNmzRKDgoJEs9lcoP+jxkHOy9lzVRRFccuWLaIgCOLVq1dLHA85t7LK29u3b4sAxP3791vaMjIyRADizp07i9yP11gqirPnqijyGlsVlVXefvXVV6Kfn59oMpksbZGRkSIAMSYmpsj9eI2lwjh7nooir69Vka1526pVK/H9998v8rhnz54VAYhHjhyxtB0+fFgEIJ4/f77I/Xh9peI4e76KIq+zVVFZ5e39li9fLrq7u5eoL6+z93DkC5UpnU4HAFAqlZY2qVQKhUKBgwcPWvVdu3YtfHx80KBBA0yaNAmZmZlFHvfnn39GcnLyQz+JdfjwYXTq1AkuLi6Wth49euDmzZu4evXqo78gqrScMVeXLVuGbt26ITQ09CGvjiore+Wtt7c3wsPDsWrVKmRnZ8NoNOKrr76Cv78/mjdvXuT5eY2lknLGXOU1luyVtzqdDgqFwuoThCqVCgAKHOd+vMZSSThjnvL6SiXJ26SkJPz999/w8/ND27Zt4e/vj06dOlnl4+HDh+Hu7o5WrVpZ2lq3bg13d3ccOnSoyPPz+kqPwhnzlddZslfe2orX2XtYfKEyVb9+fYSGhmLq1KlITU2FXq/H//73PyQmJiIhIcHSb8iQIfj++++xd+9efPDBB/jxxx/Rr1+/Io+7bNky9OjRAyEhIcWePzExEf7+/lZtd58XNz0JVT3OlqsJCQn47bffMHr06Ed5mVTJ2CtvBUHAn3/+icjISGi1WiiVSixYsAA7duwodk5XXmOppJwtV3mNJcB+edulSxckJiZi7ty50Ov1SE1NxXvvvQcAVsd5EK+xVBLOlqe8vhJQsry9cuUKAGD69OkYM2YMduzYgWbNmqFr166WNQsSExPh5+dX4Ph+fn7FXid5faVH4Wz5yussAfbLW1vxOnsPiy9UpuRyOX788UdcvHgRXl5eUKvV2Lt3L3r27AmpVGrpN2bMGHTr1g0NGzbEwIED8cMPP2Dnzp04efJkgWPGxcXh999/x8svv2zV3qBBA7i6usLV1RU9e/a0tAuCYNVP/HdxpwfbqWpztlxdsWIFPDw80Ldv39K8bHJy9spbURQxbtw4+Pn54cCBAzh69Cj69OmD3r17W/4w4zWWSsPZcpXXWALsl7cNGjTAypUr8dlnn0GtViMgIAA1a9aEv7+/5Ti8xpKtnC1PeX0loGR5azabAQCvvvoqRo0ahaZNm2LBggWoV68evvvuO8uxCsszURQt7by+Umk5W77yOkuAffP2YXidLZ7M0QFQ5de8eXOcOnUK6enp0Ov18PX1RatWrYpdfLxZs2aQy+WIiYlBs2bNrLYtX74c3t7eePbZZ63af/31VxgMBgD3hsgHBAQUqKgmJSUBQIEKLJGz5Kooivjuu+8wbNgwKBQK214sVRr2yNvdu3dj27ZtSE1NhZubGwBgyZIl+PPPP7Fy5Ur85z//4TWWSs1ZcpXXWLqfvf42GDx4MAYPHoxbt25Bo9FAEATMnz8fNWrUAMC/Y6l0nCVPeX2l+z0sbwMDAwEAjz32mNV+4eHhuH79OoD8/Lt161aBY9++fduSf7y+kj04S77yOkv3s0felgSvs8XjyBcqN+7u7vD19UVMTAyOHz+OPn36FNk3OjoaBoPBciG4SxRFLF++HMOHD4dcLrfaFhoaitq1a6N27dqoVq0aAKBNmzbYv38/9Hq9pd8ff/yBoKAghIWF2e/FUaVS0XN13759uHTpUoERNVS1lSZvc3JyAMBqnve7z+9+GobXWLKXip6rvMZSYezxtwGQ/59NV1dXbNiwAUqlEt27dwfAayzZR0XPU15fqTBF5W1YWBiCgoJw4cIFq/4XL160rGPRpk0bpKen4+jRo5btf//9N9LT09G2bVsAvL6SfVX0fOV1lgpTmrwtCV5nH0IkKqXMzEwxMjJSjIyMFAGI8+fPFyMjI8Vr166JoiiKGzduFPfs2SNevnxZ/Omnn8TQ0FCxX79+lv0vXbokfvTRR+KxY8fE2NhYcfv27WL9+vXFpk2bikaj0epcO3fuFAGIZ8+eLVFsaWlpor+/vzho0CDxzJkz4ubNm0U3Nzdx3rx5Vv3uxt+8eXNx8ODBYmRkpBgdHV3Kd4YqmsqQq6IoikOHDhVbtWpVineCnEl55O3t27dFb29vsV+/fuKpU6fECxcuiJMmTRLlcrl46tSpImPjNZbuVxlyVRR5ja1qyutvg0WLFoknTpwQL1y4IC5evFhUqVTi559/XmxsvMbSXZUhT0WR19eqprR5K4qiuGDBAtHNzU3ctGmTGBMTI77//vuiUqkUL126ZOnz1FNPiY8//rh4+PBh8fDhw2KjRo3E3r17Fxsbr6/0oMqQr6LI62xVU155e+3aNTEyMlL86KOPRFdXV8s5MzMzi4yN19l7WHyhUtuzZ48IoMBjxIgRoiiK4ueffy4GBweLcrlcrF69uvj++++LOp3Osv/169fFjh07il5eXqJCoRBr1aolvvnmm2JKSkqBcw0aNEhs27btI8V3+vRpsUOHDqKLi4sYEBAgTp8+XTSbzVZ9Cos/NDT0kd8LqtgqQ66mpaWJKpVK/Prrrx/9DSCnVF55e+zYMTEiIkL08vIStVqt2Lp1a/HXX399aHy8xtJdlSFXeY2tesorb4cNG2bp8/jjj4urVq0qUXy8xpIoVo485fW16ilt3t41a9YsMTg4WFSr1WKbNm3EAwcOWG1PSUkRhwwZImq1WlGr1YpDhgwRU1NTHxofr690v8qQr7zOVj3llbcjRowo9Dx79uwpNj5eZ/MJovjvajdERERERERERERERERUalzzhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjFl+IiIiIiIiIiIiIiIjsiMUXIiIiIiIiIiIiIiIiO2LxhYiIiIiIiIiIiIiIyI5YfCEiIiIiIiIiIiIiIrIjmaMDqMjMZjNu3rwJrVYLQRAcHQ4RERERERERERERETmQKIrIzMxEUFAQJJKix7ew+FKMmzdvIiQkxNFhEBERERERERERERFRBXLjxg0EBwcXuZ3Fl2JotVoA+W+im5ubg6MhADAYDPjjjz8QEREBuVzu6HCIisRcJWfDnCVnwVwlZ8S8JWfBXCVnxLwlZ8J8JWfDnC1cRkYGQkJCLPWDorD4Uoy7U425ubmx+FJBGAwGqNVquLm58ReeKjTmKjkb5iw5C+YqOSPmLTkL5io5I+YtORPmKzkb5mzxHrZUSdETkhEREREREREREREREdEjY/GFiIiIiIiIiIiIiIjIjlh8ISIiIiIiIiIiIiIisiOu+UJEREREREREREREVM5EUYTRaITJZHJ0KIUyGAyQyWTIy8ursDGWBalUCplM9tA1XR6GxRciIiIiIiIiIiIionKk1+uRkJCAnJwcR4dSJFEUERAQgBs3bpS6EOFs1Go1AgMDoVAobD4Giy9EREREREREREREROXEbDYjNjYWUqkUQUFBUCgUFbK4YTabkZWVBVdXV0gkVWMFE1EUodfrcfv2bcTGxqJOnTo2v3YWX4iIiIiIiIiIiIiIyoler4fZbEZISAjUarWjwymS2WyGXq+HUqmsMsUXAFCpVJDL5bh27Zrl9dui6rxjRERERERERERERFWEKIrI0RuhN5odHQoVoSoVNJyNPX42HPlCREREREREREREVMlcSsrCsaupAABBAGQSAQqZBDKJBHKpALlMAvl93yukEsil/z6/73uZ9O62/O+JqGRYfCEiIiIiIiIiIiKqRPRGM07HpVueiyJgMIkwmEwATDYfVyIAsn8LMQqpxOp7uSy/YFPdSw0vje2LlBNVFiy+EBEREREREREREVUiUTfToSuD6cbMYn5hR28Esoso4lxLyUbPhoFQyDhKhqo2/gYQERERERERERERVRKZeQbE3Mp02PmzdSYcjb3jsPNT2RAEodjHyJEjHR1ihcORL0RERERERERERESVxD830mGy/6CXR3L9Tg4uJWWhtp+rYwMhu0lISLB8v2HDBnz44Ye4cOGCpU2lUln1NxgMkMvl5RZfRcSRL0RERERERERERESVQFJmHq7fyXF0GACAk9dSkZ5rcHQYTkEURWRnZzvkIYpiiWIMCAiwPNzd3SEIguV5Xl4ePDw8sHHjRnTu3BlKpRJr1qzB9OnT0aRJE6vjLFy4EGFhYVZty5cvR3h4OJRKJerXr48lS5bY6Z11LI58ISIiIiIiIiIiIqoETl5Lc3QIFkaziL8uJaNHgwBIJYKjw6nQcnJy4OrqmFFCWVlZ0Gg0djnWlClT8Nlnn2H58uVwcXHB119//dB9vvnmG/z3v//F4sWL0bRpU0RGRmLMmDHQaDQYMWKEXeJyFBZfiIiIiIiIiIiIiJxcbHI27mTry+TYZhOQmyMgJ0uC3CwJsv/9mpMlICf77vcS5GQLcHUzo9/L6ZArgLQcA07dSEXzUK8yiYsqlgkTJqBfv36PtM/HH3+Mzz77zLJfjRo1cPbsWXz11VcsvhARERERERERERGR4xhNZvxzI63QbY9SOLF8nyVBbrZg+T4v59FWr1BrzHhmeCYA4EJiFgLcVajmoXrIXlWXWq1GVlaWw85tLy1atHik/rdv38aNGzfw8ssvY8yYMZZ2o9EId3d3u8XlKHYvvmRkZODixYsIDg5GQECAvQ9PRERERERERERERPc5n5iJHL3J8lyvAz571xeXolweuXBSHLlChNrVDLWrGSpXM9QaM1Su/7ZpzMjJkmDfNldsXemOzs9mQ+thBgAcuZyCXo0CoVJI7RZLZSIIgt2m/nKkB1+DRCIpsKaMwXBvHSCzOT8/vvnmG7Rq1cqqn1Tq/LliU/Hljz/+wPr16zF+/Hg0bdrU0r506VJMnDgRer0egiBgwoQJmDdvnt2CJSIiIiIiIiIiIqJ7cvUmnL2ZYdW2Y4Mboo5ajzSRK0SoNGZL8SS/gHKvcKJyNUNd2HPL92bIFcXHYjYD1y4qcPWiAlu+c8fwiakAAJ3RjEOXk9Glvh8Egeu/VBW+vr5ITEyEKIqWn/upU6cs2/39/VGtWjVcuXIFQ4YMcVCUZcem4su3336L7du34/PPP7e0nT17FuPHj4dEIkHr1q1x7tw5LFiwAJ06dcIzzzxjt4CJiIiIiIiIiIiIKN8/cWkwmu+NLkhNlmDrCjcAwMhJd/BEl5wSFU7sQSIBBo1Pxazx/tj5oysi+mciIMQIALiVoUP0zQw0rOb800lRyXTu3Bm3b9/GnDlz8MILL2DHjh347bff4ObmZukzffp0vPnmm3Bzc0PPnj2h0+lw/PhxpKamYuLEiQ6MvvRsGnN28uRJNG3aFFqt1tK2fPlyiKKIFStW4K+//kJkZCRcXFywZMkSuwVLRERERERERERERPnuZOsRm5xt1bZxqQfyciSo1UCHrv2y4O5VPoWXuxq21OHx1rkwmQRsWOphtS0qPh3JWbryC4YcKjw8HEuWLMH//d//oXHjxjh69CgmTZpk1Wf06NH49ttvsWLFCjRq1AidOnXCihUrUKNGDQdFbT82jXy5detWgcVzdu7cCQ8PDwwcOBAAEBoaio4dOyI6Orr0URIRERERERERERGRlcjrqbh/SY3LZxXYv90VADD87VRI7LfcyyMZ9EYazhxV4uhuNWLOKFCnkR4AYBaBvy4lo2fDQChkDgqOSm3kyJEYOXKk5XlYWFiBtV3uGjt2LMaOHWvV9t5771k9Hzx4MAYPHmz3OB3NpgyXyWTQ6/WW51lZWYiKikKHDh0gue832tfXF7dv3y59lERERERERERERERkceNODm5l3BtFIorA6gWeAID2PbNRu6G+qF3LXPXaBnR8On9EzrpFHlYFomydCceu3nFQZETlx6biS1hYGE6cOGF5vn37dphMJnTv3t2qX0pKCry9vUsXIRERERERERERERFZmM0iTt1Is2o79IcaMWdc4KIy48XX0grdryzIpAK8XRWo5atB81BP+Lu5AABeGJMOhYsZF08rcXyfymqfayk5uHw7q9xiJHIEm6YdGzhwIKZOnYrnn38enTp1wpw5c6BQKNC3b19LH1EUceLECdSrV89esRIRERERERERERFVeReTMpGZZ7Q8z8sV8P1iDwBAnxEZ8PIz2f2cEgFwU8nh/u/DQy2Hh1oBVxfrW8yeajluZSTBy8+EXoMz8dNyd6z/Pw80bZ8L2X1dT1xNha/WBW5Kud1jJaoIbCq+jB8/Hj///DO2bNmCLVu2QCKRYN68eahWrZqlz+7du3H79u0C87kRERERERERERERkW10RhOi4jOs2ratcUPqbRl8A43oOSijiD1LTuMihYdaAY/7Ci1uSjkkEuGh+/q5KeGlkeNOtgG9h2Zg90+uSLwhx+6fXBHxwr3RLkaziEOXkhHxWECJjkvkbGwqvqjVahw4cAAHDhxAUlISmjRpgjp16lj1kUqlWLBgAZ555hm7BEpERERERERERERU1UXFp0NvNFue306QYtsaLQBg8PhUKFxKfiwXmeTfESxyuKsU/36VQy61abUKi3oBbjh8OQUqjYjnR6dj+VwvbFnmjvY9s6HW3FsA5k62AZE30tA81LNU5yOqiGwqvvz888+Qy+Xo2bNnkX06d+6Mzp072xoXEREREREREREREd0nI8+AmFvWa6V8v9gDBp0E4c3y0PLJ3EL3k0kEuKvl+SNZ1HJ4/FtoUcqlZRJnqJcap26kIldvRuc+WdixUYuEa3JsW+WGAa+lW/W9kJiJAHclqnmoijgakXOyqYT53HPP4YsvvrB3LERERERERERERERUhMjraTDfGziC86dc8PcuDQSJiGFvp0K4b/YuV6UMHer44JnGgRjQMgQ9GgSgVU1v1A9wQ4C7sswKLwAgkQio658/GkcmAwa+ngYA+HW9FilJBc975HIKcvX2X6eGyJFsKr74+vrC05NDwYiIiIiIiIiIiIjKw62MPMSn3hvZYjYBq+fn36N9sk8WQusYrPo3q+6BEC81tA5a0L62nytk/67l0rxDLuo1yYNBJ8EPX7kX6KszmnH4SnJ5h0hUpmwqvnTu3BlHjx6FKIoP70xERERERERERERENhNFESevpVq17dumwdWLCqhdzej/ivVUXoEeSgR7qsszxAJcZFLU8NUAAAQBGPxGGgDgwK8aXIspWBBKTNch+mZ6gXYiZ2VT8eXjjz9GcnIy3n77beTl5dk7JiIiIiIiIiIiIiL61+Xb2UjNuTeyJSdLwMYvPQAA/Uanw83TbNkmEVBhFrCvF6C1fF+7oR6tumZDFAWs/z+PQvufiUtHSpaunKKjiqxz586YMGGC5XlYWBgWLlzosHhsIbNlp++//x69evXCokWLsH79enTr1g3Vq1eHUqks0FcQBHzwwQelDpSIiIiIiIiIiIioqjGYzDgTn2bV9tNyd2SkShEYakD3FzKtttUPdIObg6Yae5CbUo4gDyVupuV/gH/guDQc36fG6SMqnPlbiUatrD/YbxaBvy6noGfDAMilNo0boErq2LFj0Gg0jg7jkdhUfJk+fToEQYAoikhKSsK6deuK7MviCxEREREREREREZFtzt7MQK7+3siWxOsy7NiQP6Jk6FupkN13h1etkKJhkFt5h1is8EA3S/HFr5oJ3V/IxI71bli32AOftkiERGrdPyvPiGOxd9C2to8DoqWKytfX19EhPDKbii/Lly+3dxxEREREREREREREdJ9snREXEq1Htqz9wgMmo4AmbXPRpK31yJEmIR6QVbARI/5uSniq5ZZp0/qOysD+ba64HqPAwR0adHw6u8A+V1NyEOCehZq+ruUdrkOIIpCT45hzq9X5a/KUVOfOndGoUSNIpVKsXLkSCoUCH3/8MYYMGYI33ngDP/zwA/z8/LB48WL07NkTAHD27FlMmjQJ+/fvh0ajQUREBBYsWAAfn/wCW3Z2Nl577TVs3rwZWq0WkyZNKnDesLAwTJgwwTIV2fz587F8+XJcuXIFXl5eeOaZZzBnzhy4uubnzIoVKzBhwgRs2LABEyZMwI0bN9C+fXssX74cgYGBpXvTSsim4suIESPsHQcRERERERERERER3eefG2kwmkXL89NHlDh5UA2pVMSQt1Kt+vppXRDmUzGnZaoXoMWRK3cAAFp3M/qMTMf3iz2x6St3tO6aA4VSLLDP8Wup8NG6VJgp1MpSTg7g6qA6U1YW8Kizea1cuRKTJ0/G0aNHsWHDBrz22mv46aef8Nxzz+G9997DggULMGzYMFy/fh3p6eno1KkTxowZg/nz5yM3NxdTpkzBgAEDsHv3bgDAu+++iz179mDLli0ICAjAe++9hxMnTqBJkyZFxiCRSPDFF18gLCwMsbGxGDduHCZPnowlS5ZY+uTk5GDevHlYvXo1JBIJhg4dikmTJmHt2rW2vFWPrGKVQe8THx+PoUOHwtvbG2q1Gk2aNMGJEycs20VRxPTp0xEUFASVSoXOnTsjOjra6hg6nQ7jx4+Hj48PNBoNnn32WcTFxZX3SyEiIiIiIiIiIiJ6JMlZOlxNuTccwmgEVi/0BABE9M9EUKjRsk0QgBZhnuUeY0mFeWugUty7FR3RPxM+AUbcSZLht3+nUHuQ0STi0KVkmM0FCzPkWI0bN8b777+POnXqYOrUqVCpVPDx8cGYMWNQp04dfPjhh0hJScHp06exdOlSNGvWDDNnzkT9+vXRtGlTfPfdd9izZw8uXryIrKwsLFu2DPPmzUP37t3RqFEjrFy5EiaTqdgYJkyYgCeffBI1atRAly5d8PHHH2Pjxo1WfQwGA7788ku0aNECzZo1wxtvvIFdu3aV5VtjxaaRL3cZjUZs27YNx44dQ3JyMlq1aoWXXnoJAHDz5k0kJyfjscceg0z2aKdJTU1Fu3bt8OSTT+K3336Dn58fLl++DA8PD0ufOXPmYP78+VixYgXq1q2LTz75BN27d8eFCxeg1eb/wk6YMAG//PIL1q9fD29vb7zzzjvo3bs3Tpw4AalUWsTZiYiIiIiIiIiIiBwr8nqa1fNdm11x86ocWg8Tnns53WpbHT9XeKgV5Rjdo5FIBNTx0+J0XH7cChdgwNg0LJnug59XuuHJZ7Pg5mkusN+dbANOxaWhWfWKW1iyB7U6fwSKo879qB5//HHL91KpFN7e3mjUqJGlzd/fHwCQlJSEEydOYM+ePZbpwO53+fJl5ObmQq/Xo02bNpZ2Ly8v1KtXr9gY9uzZg5kzZ+Ls2bPIyMiA0WhEXl4esrOzofl3KI9arUatWrUs+wQGBiIpKenRX7CNbC6+7Nu3D8OGDUN8fDxEUYQgCDAYDJbiy65duzBy5Ehs3LgRzz///CMde/bs2QgJCbFaWyYsLMzyvSiKWLhwIaZNm4Z+/foByB/q5O/vj3Xr1uHVV19Feno6li1bhtWrV6Nbt24AgDVr1iAkJAQ7d+5Ejx49bH3pRERERERERERERGXmekoObmfqLM8z0yT44RsPAED/V9Oh0d4bDeIik6BRsHt5h/jIavu54uzNDMs0am0icvDr93pcvaDAlmXuGDEptdD9zidkItBdiUB3VXmGW64E4dGn/nIkudx6KjhBEKzahH8XkTGbzTCbzXjmmWcwe/bsAscJDAxETEzMI5//2rVr6NWrF8aOHYuPP/4YXl5eOHjwIF5++WUYDIZi4xTF8htJZVPx5cyZM+jVqxdMJhPeeusttGvXDv3797fq8/zzz2PcuHH48ccfH7n48vPPP6NHjx7o378/9u3bh2rVqmHcuHEYM2YMACA2NhaJiYmIiIiw7OPi4oJOnTrh0KFDePXVV3HixAkYDAarPkFBQWjYsCEOHTpUaPFFp9NBp7t3UcvIyACQPzzp/h8aOc7dnwN/HlTRMVfJ2TBnyVkwV8kZMW/JWTBXyRkxb8mZlDRfTWYRkdeSAfO9aZd++NoDOZkSVK+tw5O904H7Bok0DNRCIpphMBQcOVKRSAFU93TBldvZAPLXwxj8Rgpmjg/Eri2uiHghDYHVjYXuezgmCRGP+cNFXjlmMzIYDBBF0VKcqKjuFiruxnp/+4NxF9ZmNpvRtGlTbN68GdWrVy90hqyaNWtCLpfj0KFDCA4OBpA/M9bFixfRsWPHQs979OhRGI1GzJ07FxJJ/nR2GzZssJzz/vf1/v0LayuK2WyGKIowGAwFZtEq6b85NhVfZsyYAZ1Ohz/++ANdunQptI9arUZ4eDgiIyMf+fhXrlzB0qVLMXHiRLz33ns4evQo3nzzTbi4uGD48OFITEwEcG/40l3+/v64du0aACAxMREKhQKenp4F+tzd/0GzZs3CRx99VKD9jz/+gNqW8VdUZv78809Hh0BUIsxVcjbMWXIWzFVyRsxbchbMVXJGzFtyJiXN17sDIa5e1WL3T2EAgDEjjkGbkmLV73wScN6O8ZW1+wd4PBECtGjRCsePB+DHz6WYMuVkkfvtii/72MqLTCZDQEAAsrKyoNfrHR3OQ2VmZlq+NxqN0Ov1loELQH6hIi8vz6oNAHJzczFs2DB88803GDBgAMaPHw9vb29cuXIFmzdvxueffw6pVIqhQ4fi3XffhUqlgq+vLz755BNIJBKr89x/joCAABiNRsybNw9PPfUUjhw5gi+//NISq0QiQV5eHkRRtIopNzcXAArEWRi9Xo/c3Fzs378fRqN1UTAnJ6eIvazZVHzZt28fWrduXWTh5a7q1avb9I+f2WxGixYtMHPmTABA06ZNER0djaVLl2L48OGWfneHL911d/qz4hTXZ+rUqZg4caLleUZGBkJCQhAREQE3N7dHfh1kfwaDAX/++Se6d+9eYNgYUUXCXCVnw5wlZ8FcJWfEvCVnwVwlZ8S8JWdSknzVGUz4LSoRBtPdEQfANx8HwGwW0LJzNmp2rYZsVLP07xLuB29NxV3rpTAHYm4jMf3e7EP9JxhxYriIw4eDEHmrJeo20hW57+Mh7qjnry2PMMtUXl4ebty4AVdXVyiVSkeHUyRRFJGZmQmtVmu5py6TyaBQKKzul0skEiiVygL30FUqFerVq4eDBw/iP//5D1544QXodDqEhoaiR48e8PDwgCAIWLhwIcaNG4fBgwdDq9Vi4sSJyMnJsTrP/edo164dPvvsM8ybNw8zZsxAhw4dMHPmTIwcORJarRZubm5QKpUQBMEqJpUqf+q6ktzrz8vLg0qlQseOHQv8jEpSvAFsLL5kZGSgWrVqD+2n0+lgMpke2u9BgYGBeOyxx6zawsPD8eOPPwIAAgICAOSPbgkMDLT0SUpKsoyGCQgIgF6vR2pqqtXol6SkJLRt27bQ87q4uMDFxaVAu1wu5z/gFQx/JuQsmKvkbJiz5CyYq+SMmLfkLJir5IyYt+RMisvXU/GZMIiS/Dm5AJzYp0L0CRXkChGD30wDJPemP6rho0GAhxMtFPKv8GqeSMy8bXkeXNuMTr2zsfdnV6xb7I3/fn0LRX2+PupmFoI8XeHlZAWnB5lMJgiCAIlEYpk2qyK6Oz3X3VgBYO/evQX6Xb16tUDb/Wur1KtXD1u2bCnyPG5ublizZo1V2+TJk4s9x8SJE60GUgDAiBEjLN+/9NJLlvXp7+rXr1+J13yRSCSWtWwe/H0t6b83Nv1kAwMDce7cuYf2i4qKQmho6CMfv127drhw4YJV28WLFy3HqlGjBgICAqxG1ej1euzbt89SWGnevDnkcrlVn4SEBERFRRVZfCEiIiIiIiIiIiJyhPQcAy4lZVmeG/TA2i88AAC9BmfAL+jeh9zlUgFNq3uUc4T2Eeiugofa+ub1C6+kwUVpRswZFxzboypyX7MI/HUpGQZTxV0nhegum4ovERERiI6OLrZatWLFCly7dg1PP/30Ix//7bffxpEjRzBz5kxcunQJ69atw9dff43XX38dQH6lbcKECZg5cya2bNmCqKgojBw5Emq1GoMHDwYAuLu74+WXX8Y777yDXbt2ITIyEkOHDkWjRo3QrVs3W142ERERERERERERUZk4eT0V938of8d6LZLi5fDwMeLZ4dbTHDWs5g6lEy8+Xy/AeuowTx8zeg3JX1dk/RIPGItZzzwzz4jjV1PLMjwiu7Cp+PLee+/B1dUVgwYNwgcffIDjx48DyF9oJioqCp988gnGjRsHb2/vAkN/SqJly5bYsmULvv/+ezRs2BAff/wxFi5ciCFDhlj6TJ48GRMmTMC4cePQokULxMfH448//oBWe+8Xd8GCBejbty8GDBiAdu3aQa1W45dffoFU6rwXJiIiIiIiIiIiIqpcbqblIiE9z/I8NVmCn1a4AwAGvp4GpfpeVcZNJXP6dU/CvDVQyq1vTfcekgF3LxNuxcmxa4trsfvHJmfjanJ2WYZIVGo2FV9CQ0Oxfft2eHp64tNPP0WrVq0gCAI2bdqExo0b48MPP4RWq8XWrVst67M8qt69e+PMmTPIy8vDuXPnMGbMGKvtgiBg+vTpSEhIQF5eHvbt24eGDRta9VEqlVi0aBFSUlKQk5ODX375BSEhITbFQ0RERERERERERGRvoigi8nqaVdvGpR7Iy5GgVgMd2vXIsdrWItQLEkkRi6I4CalEQB0/6wKSUi3i+THpAIDNy9yRk1X8azx69Q4y84oZIkPkYDav5tO+fXtcvHgR8+fPR8+ePREeHo66deuiS5cumDVrFi5cuMC1VYiIiIiIiIiIiIiKcSkpC+m594oIl88qsH97/siP4W+n4v712EO8VAhwV5Z3iGWijr8rpA/cne78TBaCwgzISpfi51Vuxe5vNIk4dDkFZnPJFlAnKm+y0uys1WoxYcIETJgwwU7hEBEREREREREREVUNeqMZp+PSLc9FEVi9wBMA0L5nNmo31Fu2ySQCmlX3LPcYy4pSLkWYtwaXb9+bPkwqAwa9nobP3vXFjg1adH8+C97+piKPkZKlR9TNdDwe7FEOERM9GptHvhARERERERERERGR7aJvpkNnNFueH/pDjZgzLnBRmfHia2lWfR8LcoPGpVSfpa9w6gcUHN3StH0uwpvmwaCTYNNX7g89xvmETOTqiy7QEDmKTb+t169fL1E/hUIBT09PuLi42HIaIiIiIiIiIiIiokopS2fExVuZlud5uQLW/58HAKDPiAx4+d0rKGhcpAgPLH4aLmfkrpYj0F2JhPQ8S5sgAIPfTMMHowJw8DcNeg7KRGidotd2MZpFRN1MR8swr/IImajEbCq+hIWFQRBKtqiTIAioV68ehgwZgnfeeYeFGCIiIiIiIiIiIqryTl1Pg+neoBdsW+OGO0ky+AYa0XNQhlXfZtU9IZWU7H6ss6kfqLUqvgBAzXA92kRk4/AfGqxb5IGpX9wu9hiXk7JQP0ALrVJelqESPRKbii8dO3aEwWDA4cOHAQDe3t6oXr06RFFEXFwckpOTAQCtW7dGcnIyYmJi8MEHH+Cnn37C3r17oVar7fcKiIiIiIiIiIiIiJxIUmYert/JsTy/nSDFtjVaAMDg8alQ3Pf59UB3JUK8Ku/91EB3FdxVcqTnWo9uGTA2Dcf2qBF1VIXTR5R4vHVeEUcAzCJwJi4dbWv7lHW4ZWrd3yWbccpeBreq/kj9O3fujCZNmmDhwoVlE1AhRo4cibS0NPz000/ldk57sWnNl23btsFoNKJ58+bYvXs3bt++jRMnTuDkyZNISkrC7t270bJlSxiNRpw8eRKxsbHo2rUrTpw4gfnz59v7NRARERERERERERE5jZPX0qyer/8/Dxh0EoQ3y0PLJ3Mt7RIBaBbqWc7Rlb96AdoCbX5BJnR/IX9atnWLPGB+yLIu1+7kIC1HXxbhUQW3ceNGNGnSBGq1GqGhoZg7d26BPvv27UPz5s2hVCpRs2ZNfPnll2Uel03Flw8++ABXrlzBzp070blz5wLbO3fujN9//x2xsbGYNm0aqlWrhrVr10Kr1eKHH34obcxERERERERERERETulaSg7uZN8rEpw/5YIjOzUQJCKGvZ2K+1d7qBughbuq8k+lVcNHAxdZwVvVfUdmQK0148ZlBQ78pin2GKII/BOXXlYhUgX122+/YciQIRg7diyioqKwZMkSzJ8/H4sXL7b0iY2NRa9evdChQwdERkbivffew5tvvokff/yxTGOzqfjyww8/4Mknn4S7u3uRfTw8PPDkk09i8+bNAABfX180a9YMly9fti1SIiIiIiIiIiIiIicXffNegcBsAlbPzx/Z8mSfLKuF5VUKCRpVK/r+a2UilQio4+9aoN3V3Yy+I/Pfr01fuUOXV/y6N/GpubidqSuTGKkgvV6PyZMno1q1atBoNGjVqhX27t0LAEhPT4dKpcKOHTus9tm8eTM0Gg2ysrIAAPHx8XjxxRfh6ekJb29v9OnTB1evXi1xDKtXr0bfvn0xduxY1KxZE08//TSmTJmC2bNnQxRFAMCXX36J6tWrY+HChQgPD8fo0aPx0ksvYd68eXZ5H4piU/ElOTkZubm5D+2Xm5trWf8FAPz9/WEyPWR8GBEREREREREREVElla27d390/3YNrl5UQO1qRv9XrEdtNAnxhFxq0+1bp1TXX4vCXm5E/0z4BhqReluG374vOD3Zg07Hpdk/OCrUqFGj8Ndff2H9+vU4ffo0+vfvj6eeegoxMTFwd3fH008/jbVr11rts27dOvTp0weurq7IycnBk08+CVdXV+zfvx8HDx6Eq6srnnrqKej1JZtCTqfTQalUWrWpVCrExcXh2rVrAIDDhw8jIiLCqk+PHj1w/PhxGAzWaw3Zk02/vbVq1cKuXbtw8eLFIvtcvHgRu3btQq1atSxtCQkJ8PFx7kWPiIiIiIiIiIiIiB5Vnt76Q+k5WQI2LPUAAPR7OR1unmbLNl+tC2r4FD/NVmWjlEtR3avga5YrgAGvpQEAflnthvQ7xd/SvpWhQ0L6wwcOUOlcvnwZ33//PTZt2oQOHTqgVq1amDRpEtq3b4/ly5cDAIYMGYKffvoJOTk5AICMjAxs374dQ4cOBQCsX78eEokE3377LRo1aoTw8HAsX74c169ft4ygeZgePXpg8+bN2LVrF8xmMy5evIiFCxcCyK9HAEBiYiL8/f2t9vP394fRaLQaPGJvNhVfXn/9deTl5aFDhw6YO3cuYmNjYTAYYDAYEBsbi7lz56Jjx47Q6XQYN24cACAvLw+RkZFo1qyZXV8AERERERERERERUUV35qb1yJaflrsjI1WKwFCDZWF5ABAEoHmoZ3mHVyGEBxY+sqV1txzUDNchL0eCLd89fCq2f25w7ZeydvLkSYiiiLp168LV1dXy2Ldvn2XpkaeffhoymQw///wzAODHH3+EVqu1jEI5ceIELl26BK1Wa9nfy8sLeXl5JV6+ZMyYMXjjjTfQu3dvKBQKtG7dGgMHDgQASKVSSz9BsJ6y7u6UZA+225PMlp1ee+01nD9/HosWLcJ//vMf/Oc//7EEeTdoURTx+uuvW4ov165dw+DBg/H888/bKXQiIiIiIiIiIiKiiu9Oth7XUnKg/vd54nUZdmzILzQMfSsVMvm9vrX9XOGlUZR/kBWAh1qBAHcXJKZbr9sikQCDx6fhk3H+2L3FFT0GZCKwurHI49zJ1uPGnRyEeKmL7EOlYzabIZVKceLECasiBwC4uuav36NQKPDCCy9g3bp1GDhwINatW4cXX3wRMpnMcozmzZsXmJoMyF9DviQEQcDs2bMxc+ZMJCYmwtfXF7t27QIAhIWFAQACAgKQmJhotV9SUhJkMhm8vb0f6XU/CpuKLwDw+eefY8CAAVi6dCkOHz6MmzdvAgACAwPRtm1bvPLKK+jYsaOlf7169bB06dLSR0xERERERERERETkJPRGM/66lIx/P7MOAFj7hQdMRgGN2+SiSds8S7tCJkGjag8f2VGZ1QtwQ2L67QLt4c10aNY+BycPqrH+/zzw9uzip4v6Jy4NwZ6qMh3ZUJU1bdoUJpMJSUlJ6NChQ5H9hgwZgoiICERHR2PPnj34+OOPLduaNWuGDRs2wM/PD25ubqWKRyqVolq1agCA77//Hm3atIGfnx8AoE2bNvjll1+s+v/xxx9o0aIF5HJ5gWPZS6lWbGrXrh3WrFmDy5cvIzc3F7m5ubhy5QrWrFljVXghIiIiIiIiIiIiqooOX0lBZt69URqn/1bh5EE1pFIRQ99KterbONgdSrn0wUNUKdU8VHBTFT5mYODraRAkIo7vU+PCKZdij5ORa0RscnZZhEgA6tatiyFDhmD48OHYvHkzYmNjcezYMcyePRu//vqrpV+nTp3g7++PIUOGICwsDK1bt7ZsGzJkCHx8fNCnTx8cOHAAsbGx2LdvH9566y3ExcWVKI7k5GR8+eWXOH/+PE6dOoW33noLmzZtsqz7AgBjx47FtWvXMHHiRJw7dw7fffcdli1bhkmTJtnt/SiMzSNfiIiIiIiIiIicQVpaGo4ePYojR44gJiYG77zzDpo0aeLosIioCoiKT0d86r3F341GAWu+8AIARPTPRFDYvaKMp1qO2n6u5R5jRVQ/QIujsakF2qvVMOLJZ7Ow+yct1i32wPRvbqG4gS1n4tMR5q2BROIco18Gt6ru6BAeyfLly/HJJ5/gnXfeQXx8PLy9vdGmTRv06tXL0kcQBAwaNAhz587Fhx9+aLW/Wq3G/v37MWXKFPTr1w+ZmZmoVq0aunbt+kgjYVauXIlJkyZBFEW0adMGe/fuxRNPPGHZXqNGDfz66694++238X//938ICgrCF198UeZLpJS6+GI0GpGSkgKdTldkn+rVnStpiIiIiIiIiMg5mUwmREdH48iRI5bHuXPnrPqcOXMGJ0+ehERSqglBiIiKFZ+WizPx1gu/79gRhvhYBbQeJjz3svW25mGenCLrX2HeGvxzIx06o7nAtufHpOOv3zW4FOWCo7tVaNU1t5Aj5MvWmRCTlIV6AdqyDLfK2Lt3r9VzuVyOjz76CB999FGx+82ZMwdz5swpdFtAQABWrlxZ5L4rVqwo9tg+Pj44fPhwsX2A/BE4J0+efGg/e7K5+LJz50588sknOHLkCAwGQ5H9BEGA0Vj04kdERERERERERLZKTEzE33//bSm0HDt2DNnZBaeZqVWrFlq1aoWff/4Z//zzD3766Sf069fPARETUVWQmWfAoQfWeclMl2D9+voAgP6vpkOjvbcxzFsNP62yvMOssGRSCWr7uSL6ZkaBbR7eZjw9JAObv/XAhqUeaN4xF7Jilu2IvpmOWr4ayKQsuFP5sqn4sm3bNjz33HMwmUzw9PREzZo14erKIXFEREREREREVHZ0Oh0iIyMthZa///4bV69eLdBPq9XiiSeeQOvWrdG6dWu0atUKvr6+AIAPPvgAn3zyCf773/+ib9++HP1CRHZnNJlxICYZBpNo1f7jt57IylKgem0dnnw2y9IukwpoWt2zvMOs8Or6a3EuIQNmseC2pwdnYvcWLW7FybFzsxZPvZhZ5HHyDGacT8xEw2ruZRgtUUE2FV8++ugjmM1mLFy4EK+//jqk0qq9CBQRERERERER2Zcoirh69aqlyHLkyBFERkZCr9db9RMEAQ0aNLAUWlq3bo369esXea9i4sSJ+OKLLxAVFYVNmzbhxRdfLI+XQ0RVyN+xd5CWYz1T0PVLcuzckj/11bAJdyC57xLVMMgdKgXvrz5IpZAi1FuD2OSCoxmVahHPj0nDsv95Y8syN3TolWU1kuhB5xIyUMffFS4yvs9UfmwqvkRHR6NNmzZ488037R0PEREREREREVVBmZmZOH78uNVaLUlJSQX6+fr6Wo1oadmy5SMtyuvp6YmJEydi+vTpmD59Ol544QV+qJSI7OZcQgaupeRYtYkisGahJ0SzgDZtbuKxZjoA+dcdN5UM9bkeSZHqB2gLLb4AQKfe2dixQYv4WAW2rnDH4PFpRR7HYBJxLiETTUI8yiZQokLYVHxxdXWFv7+/vWMhIiIiIiIioioiOzsbGzZssBRaoqOjYTZbL6wsl8vRtGlTtGrVylJwqVGjRqkXpJ4wYQIWLlyI8+fPY/369RgyZEipjkdEBAC3MvJw6kZagfaju1WIPq6EXGHGyJHRAGpbtjUP9YREUrprWmXmqVHA380FtzJ0BbZJZcDg8WmYO9EPv2/UotvzmfALMhV5rIuJmajnr61Qo4xEsejROuRY9vjZ2FR86datGw4fPgyz2cy5UYmIiIiIiIjokRiNRnTp0gVHjx61aq9evbrV9GFNmzaFUmn/Bajd3d0xadIkvP/++/joo4/w4osvQiaz6RYJEREAIFtnxMGYZDx4vzY9RYLlc70AAL2HpsPfPwd3x3EEe6oQ6K4q30CdUL0AbaHFFwBo3CYPDZ/IRdRRFdb/nwfe/DSlyOMYzSKibqajZZhXWYVaYnK5HACQk5MDlYo5UBHl5OSPYLv7s7KFTX9ZzJ49Gy1btsQ777yDefPmcXguEREREREREZXY3LlzcfToUbi7u2Ps2LGWKcQCAwPLLYY333wTCxYsQExMDNauXYsRI0aU27mJqHIxmUUciEmGzmg9ek8Uge/meCEzTYrqdfToOyINSM3fJpUAzUI9yz9YJxTsqYZWKUNmnrHANkEAhryZhveGK/H3Lg0uDMhEvcb6Qo6S73JSFuoHaKFV2n5D3R6kUik8PDws02uq1epSj+osC2azGXq9Hnl5eVVmEIYoisjJyUFSUhI8PDxKVfuwqfiyfPly9OzZE1988QW2bduGzp07Izg4uNAEEQQBH3zwgc0BEhEREREREVHlER0djenTpwMAFi1ahGHDhjkkDq1Wi8mTJ2PKlCmYMWMGBg8eXKpPtxJR1XX86h3cyS54w//gDjWO71NDKhPx2ocpkN13iQkPdIOrC0fclVT9AC2OXU0tdFv12gY8+WwWdv+kxZrPPfHRt7dQVJ3ALAJn4tPRtpZPGUZbMgEBAQBQ6PpmFYUoisjNzYVKpaqQxaGy5OHhYfkZ2cqm3/Dp06dDEASIoojLly/j8uXLRfZl8YWIiIiIiIiIgPzpxl566SXo9Xr07t0bQ4cOdWg8r7/+OubNm4crV65g1apVePnllx0aDxE5n0tJmbh8u+CC8ClJUqz6LH96q+dHp6N6HQPw78AYjYsUjwW6lWeYTq+Gjwb/xKVD/8DoorteGJOOQ39ocOWsCw7/oUa7p3KKPNa1lBw8FqiHh1pRVuGWiCAICAwMhJ+fHwwGg0NjKYrBYMD+/fvRsWPHKvUBBblcbpfZvmwe+UJERERERERE9Cjmz5+Po0ePwsPDA1999ZXDP0Wr0Wjwn//8B++88w4+/vhjDBs2DAqFY2/GEZHzSM7S4XghozFEEfh2phdysiSo9ZgOvYdmWG1/vJoHZNKqMYWTvcikEtT2c8XZmxmFbnf3NqPPyAxsWOKB9Us80KJzLlyUhS+YLorA6bh0dKzrW5Yhl5hUKq2wy3pIpVIYjUYolcoqVXyxF5uKL5wHlYiIiIiIiIgexblz5/Dhhx8CABYsWICgoCAHR5Rv7NixmDt3Lq5du4bvvvsOY8eOdXRIROQE8gwmHIxJhrmQ+/t7tmpw+ogKchczxn6YAukDd2CDvbjAui3q+rvifEJGoe85ADz1YgZ2bXZFcqIMv67T4rmXCi/UAEBcai6Ss3TwcXUpo2iJAJZYiYiIiIiIiKhMmUwmjBo1CjqdDj179qxQH+pUq9WYOnUqAODTTz9FXl6egyMioorObBZxMCYZOXpTgW23b0qx9gtPAMCAV9MRFHZvkXhJ1Voyw+7UChmqe6mL3K5wAQa+ngYA+GW1G1JvFz+a5J8baXaMjqigUhVfjEYjfvrpJ0ybNg2vvvoqvvvuO8u2mzdv4vTp0zAajcUcgYiIiIiIiIgquwULFuDvv/+Gm5sbvv76a4dPN/agV155BUFBQYiLi8O3337r6HCIqIKLvJGKpExdgXazGfjqE2/k5UhQr0kennox02p7LT/X8gqx0qr/kLVyWnfLQZ1GOuhyJdj0tXuxfW9l6JCYzoI7lR2biy/79u1DzZo18fzzz2PWrFn49ttvcfDgQcv2Xbt2oWnTpti6datdAiUiIiIiIiIi53P+/Hm8//77APKLMMHBwQ6OqCClUolp06YBAGbOnInc3FwHR0REFdXV5GxcSMwqdNsfm1xx7qQSLkozXn3/DiT3DbxQyiV47CGFA3o4L40CftqipwoTBGDIW/nr8OzfpsHVC8WvU3KKo1+oDNlUfDlz5gx69eqFpKQkvPXWW9i0aRNE0Xqyveeffx5qtRo//vijXQIlIiIiIiIiIudiMpnw0ksvQafToUePHhg1apSjQyrSyy+/jOrVqyMhIQFfffWVo8MhogooNVuPo7F3Ct2WcF2GDUs8AACDx6fBP9h6NqAmIR5QyLgChD3UC9AWu71OQz3aRGRDFAWs+dwTYhFrxADAnWw9btzJsXOERPls+o2fMWMGdDodfv31V8yfPx/PP/98gT5qtRrh4eGIjIwsdZBERERERERE5Hy++OILHD58GFqtFt98802Fm27sfi4uLpYROrNmzUJ2draDIyKiikRnNGF/zG0YC1nt3WwCvvrYG3qdBA1b5qJrP+uRMT6uCtT05ZRj9hLsqYKrUlZsn4Hj0iB3MePcSSVOHFAV2/d0XHqBgQVE9mBT8WXfvn1o3bo1unTpUmy/6tWr4+bNmzYFRkRERERERETOKyYmBu+99x4A4LPPPkNISIiDI3q4kSNHokaNGkhKSsLSpUsdHQ4RVRCiKOLQpRRk60yFbt++TouYMy5QacwYM+0O7q8zCwLQIsyrnCKtGgRBQD3/4ke/+ASY0Gtg/po7677wgNFQdN/0XANik1lwJ/uzqfiSkZGBatWqPbSfTqeDyVT4RYmIiIiIiIiIKieTyYRRo0YhLy8P3bp1w+jRox0dUonI5XJ88MEHAIDZs2cjK6vwdR2IqGo5HZeOhCIWZr9xWY4fvvYAAAx7OxU+Adb3Qmv5usJLoyjrEKucWr4ayKXFj6Z8ZngG3L1MuBUnx58/Fl+sOROfDnMho5qISsOm4ktgYCDOnTv30H5RUVEIDQ215RRERERERERE5KQWL16Mv/76C66urvj2228r9HRjDxo2bBhq1aqF5ORkLF682NHhEJGD3biTg+ibGYVuMxqBL2d4w2gQ0LRdLjo+bT16QiGT4PFg9/IIs8qRSSWo7Vf8VG4qjYj+r6YBADYvc0dmetG3wrN1Jly6zYI72ZdNxZeIiAhER0djy5YtRfZZsWIFrl27hqefftrm4IiIiIiIiIjIuVy6dAlTp04FAMybN8/pPpQpk8nw3//+FwAwd+5cZGQUftOViCq/9FwDDl9JKXL71hVuuHpBAY2bCS9PTcGDdebGwe5QyqVlHGXVVS9AC8lDavudemejem09cjIl2LKs+EJYVHw6jCazHSOkqs6m4st7770HV1dXDBo0CB988AGOHz8OAMjJyUFUVBQ++eQTjBs3Dt7e3pg4caJdAyYiIiIiIiKiislsNuPll19Gbm4uunTpgldeecXRIdlk0KBBqFevHu7cuYPPP//c0eEQkQMYTGYciLkNo6nwqahiz8uxdXn+zfxR76bC08f6pr2XRv7QkRlUOmqFDCFe6mL7SKTAkLdSAQA7f3TFzWuyIvvmGcw4n5hp1xiparOp+BIaGort27fD09MTn376KVq1agVBELBp0yY0btwYH374IbRaLbZu3YqAgAB7x0xEREREREREFdCSJUuwf/9+aDQaLFu2zKmmG7vf/aNfPvvsM6SlpTk2ICIqd0eupCAj11joNoM+f7oxk0nAE11y0LpbToE+zUO9nPYa6EzqBxS/lgsANGypQ7P2OTCZBHy/yKPYvucSMqAzcg1zsg+bii8A0L59e1y8eBHz589Hz549ER4ejrp166JLly6YNWsWLly4gLZt29ozViIiIiIiIiKqoK5cuYIpU6YAAObMmYOwsDDHBlRKAwYMwGOPPYb09HQsWLDA0eEQUTmKvpmOG3dyi9z+4zfuiLuigJunCaPevVNgurEaPhr4al3KOEoCAG9XlxK914PGp0EqFXHyoBpRx4rubzCJOJfA0S9kHzYXXwBAq9ViwoQJ2LZtG6KionDu3Dn8+eefmDJlCjw8POwUIhERERERERFVZHenG8vJyUHnzp0xduxYR4dUalKpFB999BEAYMGCBbhz546DIyKqnK4mZ+PIlRRcT8mB3uj49TYS0nNxOi69yO0xZxTYttYNAPDyf+7AzdM6ZrlUQNPqHmUZIj2gYTW3h/YJCjWi2/NZAIC1n3vCXMzglouJmcjVc/QLlV6pii9ERERERERERF9++SX27t0LtVqNZcuWQSKpHLcb+vXrh8cffxyZmZn47LPPHB0OUaWiN5rx16VkHLqcgiu3s3HwUjJ+PBmHP8/eQlR8Ou5k68s9piydEX9dSoFY+DIv0OUJ+HKGN0SzgPY9s9GiU8HRMQ2ruUMpl5ZxpHS/QHcVQrxUD+333Mvp0LiZcP2SAvu2aYrsZzSLiL5ZdAGOqKRs+msoJiYGq1atQmxsrFX70aNH0aZNG7i6uqJBgwbYunWrXYIkIiIiIiIiooopNjYWkydPBgDMnj0bNWvWdHBE9iORSCyjXz7//HMkJyc7OCKiyuFWRh5+PZOAaynWa6WIInA7U4fTcenYEZWILZFxOHw5BddSsst8HQ6jyYwDF28XO/pm41J3JN6Qw9PXiOFvFxwN566So57/w9cgIftrHuoJmbT4NXa07mY891IGAGDTVx7IzS66/6WkLGTpCl/zh6ikbCq+fPbZZ3jppZcgk8ksbbdv30ZERAT+/vtv5Obm4ty5c+jfvz/++ecfuwVLRERERERERBWHKIoYPXo0srOz0bFjR4wbN87RIdldnz590LRpU2RnZ2Pu3LmODofIqZnMIk5cS8Wuc0nIKcG0Trl6M2KTs/HXpRRsPhmPP6ITERWfjpQsnd1jO3r1DlJzDEVuP3vCBTs25E9vNea9O9C4FRwe0yLMExJJ8QUAKhtqhQyNqrk/tF/35zMREGJA+h0pfllV9HRlZhE4U8z0c0QlYVPx5eDBg3j88ccREhJiafvuu++QkZGBd955B7m5udiyZQtMJhOH5RIRERERERFVUl9//TV2794NlUpVqaYbu58gCJgxYwYAYPHixbh165aDIyJyTqnZeuyISsSFRNsWMxdFIDlLj9Nx6fg9+hY2n4zDocvJuJqcjTxD6UbFXEjMxNXknCK352YL+PoTbwBAl76ZaNwmr0Cf6l5q+LspSxUHlU49fy081PJi+8jkwKDxaQCAX793w+2EoqeIu5qSjfRiCnJED2PTX0UJCQkIDQ21avvtt9/g4uKC//73v1AoFOjTpw9at26NI0eO2CVQIiIiIiIiIqo4rl27hkmTJgEAZs2ahdq1azs4orLz9NNPo2XLlsjJycGcOXMcHQ6RUxFFEWdvZuD36ESk59rvRnaewYyryTk4dDkFWyLj8Xt0Is7EpSM5SwexqEVbCpGUmYfI66nF9lm3yAO3E2TwDTRi8L837u8nkwhoWt3jEV8B2ZtEIqBFmOdD+zXvkIvwZnkw6AVsWOpRZD9RBP6JS7NfgFTl2FR8ycvLg1J5r5JrMplw/PhxtG7dGq6urpb2sLAwxMfHlz5KIiIiIqIqIicnB3v37uXf0URUod2dbiwrKwvt27fH+PHjHR1Smbp/9MuSJUuQkJDg4IiInEO2zohd55Jw6kYazCWvhzwyUQRSsvQ4E5+OP6JvYfPJeBy69PBRMbl6Ew7GJBcb2+kjSuz+KX8dl1feT4FKU7DzY0Fu0LjICrRT+fPTKlHDR1NsH0EAhr6VCkEQcfgPDWKiFEX2jUvNRXIZTHNHVYNNxZeQkBCcP3/e8vzAgQPIycnBk08+adUvNzcXGk3xyV6Y6dOnQxAEq0dAQIBluyiKmD59OoKCgqBSqdC5c2dER0dbHUOn02H8+PHw8fGBRqPBs88+i7i4uEeOhYiIiIioLImiiDNnzmDevHno3r07vLy88OSTT6JDhw7Q6/WODo+IqFDffvstdu7cCaVSie+++65STjf2oB49eqBNmzbIy8vDrFmzHB0OUYUXm5yNX88kICmz/G9c64xmXE2xHhVzOi7NalSM2Sxif8xt5BnMRR4nO0PA1596AQB6DMjEY80LvhZXpQzhgUWvHULlr2l1Dyhkxf+7FFbPgI5PZwMA1n7uieIGS53m6BeykU1/HXXt2hWnT5/G559/jtOnT+P999+HIAjo06ePVb8zZ85YrQvzKBo0aICEhATL48yZM5Ztc+bMwfz587F4FvI8TAAAmpZJREFU8WIcO3YMAQEB6N69OzIz780ZOWHCBGzZsgXr16/HwYMHkZWVhd69e8NkKt0ckEREREREpZWSkoL169dj1KhRCA4OxuOPP453330XO3fuhE6ngyAIiI2NxerVqx0dKhFRAdevX8c777wDAJg5cybq1Knj4IjKx/2jX7766it+wJOoCDpj/miSw5dTYDCV4XCXEro7KiYqPsNqVMxfl5ORklX8B11WLfBC6m0ZAkIMeHFcWqF9mod6QioRyiByspVSLkXjYPeH9uv/ajpcVGbEnHHBkZ3qIvslpuuQmF5wnR+ih7Gp+DJ16lR4eXlh4sSJaNq0KQ4dOoQBAwagcePGlj7R0dG4fPky2rVrZ1NgMpkMAQEBloevry+A/E8GLly4ENOmTUO/fv3QsGFDrFy5Ejk5OVi3bh0AID09HcuWLcNnn32Gbt26oWnTplizZg3OnDmDnTt32hQPEREREZGtjEYj/vrrL3z44Ydo1aoVfH19MWjQIKxYsQI3b96ESqVCz549sXDhQpw7dw7z5s0DkL+GgtFodHD0RET3iKKIMWPGIDMzE23btsWbb77p6JDKVdeuXdGxY0fo9XrMnPn/7N11WFTp+8fx99AhLanY3a661tqCgIG6ytqx6rrq2h2rrB1rra7d3R3YudjdgR0gohLSM/P7w5/zXVYxEDjE/bouLmTmzDkf8GGYOfd57mes0nGESHOeh0ax68pzHr1KfPF6pb2fFfP4VdQntzt7xJTju81R6Wn5dXgIxiYfFpJcrE3IZm2aUlHFN8jnkAW7LIm3EwOwsVdTv3UYAGv+tib2E5O0ZO0XkRRJakaYPXt2Ll68yPz58wkODqZMmTK0a9cuwTYXLlzA29sbHx+fJAW7c+cOLi4uGBsbU758ecaOHUuePHm4f/8+gYGBuLu767Y1NjamWrVq+Pv707lzZ86dO0dcXFyCbVxcXChWrBj+/v7UqVPno8eMiYkhJuZ/v2VhYe9++eLi4oiLS74FwUTSvf9/kP8PkdbJWBXpjYxZkV6kp7H68OFD9u3bx969ezl06BChoaEJ7i9WrBju7u64ublRuXLlBGsq/vzzz4wbN46AgABWrlxJixYtUju+SEbpadyKzO1LxurixYvZu3cvJiYmzJ07F41Gg0aTeMuejGj48OHUrl2bBQsW0KdPH3LmzKl0pExNnmPTBrVGy5Wnb7gT9FbpKMki/I0eC8e/azdWr0Uo+YtGwX+e6vT1oGS2LF819mS8pq7S2S04cOPFJ1uKeTV7w8EtWXgZaMDu1VnwbhP60e1CwqJ48CKMbDaZq9gmY/bjvvTnodJqPzX8lLF7924iIyMpUKAAQUFBjB49mps3b3Lt2jVu3bpF5cqVefr0KS4uLrrH/PLLLzx8+JA9e/awatUq2rdvn6CQAuDu7k7u3LmZO3fuR4/r6+vLH3/88cHtq1atwsws8alnQgghhBBCxMTEcOXKFS5evMiFCxd4+vRpgvstLCwoWbIkpUuXplSpUtjZ2X1yf+vXr2flypW4uroyffr0TLGeghAibXv58iU9evQgMjKSdu3a0bBhQ6UjKeb333/nypUruLm50a1bN6XjCCGSkVYLkyaVxd8/GzlyhDF58hEMDTNXkTmzOXw4O9OmlcHEJJ45c/ZjbZ366xSJ9CUyMpIWLVoQGhqKpWXiaz4laebL57x8+RIbGxv09fWT9HhPT0/dv4sXL07FihXJmzcvS5cupUKFCsC7Xqv/ptVqP7jtvz63zeDBg+nTp4/u67CwMFxdXXF3d//kD1Gknri4OPbt24ebmxuGhoZKxxEiUTJWRXojY1akF2lprGq1Wq5evaqb3XL8+HFiY//XN1xfX5/y5cvj5uaGu7s733333Ve9Pq5cuTI7duzg8ePHxMXF0ahRo5T4NkQqSEvjVohP+dRY1Wq1eHt7ExkZSfny5Zk9e3aS3/NnBFZWVtSoUYNDhw4xY8YM8uTJo3SkTEueY5Wj1Wq5FRTOtadhaNLcpd1Jd2KfOf7+Dujra/nFN5zYbMX478ow5sb6uBdxxED/6y6OkfGa+mLjNey5Fkh0XOIFtDJNIM+eGO7dMGbppkp0HBSS6Lbf57Ylp13muUhfxuzHve+Y9TlJKr6cPXuWXbt20aRJE4oUKaK7fdu2bXTu3JkXL15gYWHB6NGj+e2335JyiATMzc0pXrw4d+7c0V1ZExgYiLOzs26bFy9e4OjoCICTkxOxsbG8fv0aGxubBNtUqlQp0eMYGxtjbGz8we2GhoYyuNIY+T8R6YWMVZHeyJgV6YVSYzUkJIR9+/axZ88e9u7dy7NnzxLcnzNnTurUqUOdOnWoWbMm1tbWST5W1qxZ6dGjB6NGjWL8+PE0bdr0sxcbibRNnmNFevGxsbpkyRL8/PwwNjZm8eLFCVolZkbVq1fH3d2dvXv3MmHCBBYtWqR0pExPnmNTV0RMPCcCXhEcHgMqfcggL1Fev9RjyeR3s5O924WRu4ga+LDQ/F2urJiafHgO8UvJeE09hoZQOldWTgQkXlDR04NWPd8w8ldHDu+wwL3pW3Lk/3hbqWuBEeRxsERPL4MM+i8kYzahL/1ZJKl3wYwZMxgzZgwODg662x4+fIiPjw9BQUE4OTkRERFBz549OXbsWFIOkUBMTAw3btzA2dmZ3Llz4+TkxL59+3T3x8bGcuTIEV1hpUyZMhgaGibY5vnz51y9evWTxRchhBBCCCH+6+TJk/z+++98//332Nvb07x5c5YsWcKzZ88wNTXF09OT6dOnc/PmTe7fv8/cuXNp3LjxNxVe3uvZsyfm5uZcuHABPz+/b/9mhBAiCZ4+fUqvXr0AGDlyJIULF1Y2UBrxvm35smXLuHPnjsJphEg994Ij2HXl+bvCSwai1cLCcXZEhOmTq0As3u0/vvaHk5UxrraZZ+ZDRpA7qzkOFp8ulhUsFUP5Wm/RalSs/Ms60XVi3saouRsckQIpRUaUpOLLyZMnKVWqFFmzZtXdtnDhQmJjY5k8eTJPnz7lzJkz6OvrM3Xq1K/ef79+/Thy5Aj379/n1KlTNGnShLCwMNq2bYtKpaJXr16MHTuWzZs3c/XqVdq1a4eZmZluIVIrKys6dOhA3759OXDgABcuXKBVq1YUL16c2rVrJ+VbFkIIIYQQmdDgwYOpWLEio0eP5syZM2i1WooXL06/fv3Yt28fr169YteuXfTo0YOCBQsm+8wUOzs7unTpAsCoUaNIg8s1CiEyOK1WS+fOnQkNDeX7779P0Ko7s6tQoQJeXl6o1WpGjRqldBwhUlx0nJpjd4I5ee8V8eqM95rk6E5zLvxjioGhll9HhGDwkX5Beiook9M29cOJb1Yuly2fm6zSrOsbDAy1XD1jykX/xGd4XnsWSrxa1gESn5ek4ktQUBA5cuRIcNvevXvJkiWLbqG50qVL88MPP3Dx4sWv3v+TJ09o3rw5BQsWpHHjxhgZGXHy5Ely5swJwIABA+jVqxddu3albNmyPH36lL1792JhYaHbx9SpU2nYsCE+Pj5UrlwZMzMztm/fnql70gohhBBCiC83ffp0xo8fD0DTpk1ZvHgxT58+5fLly0yaNInatWunStudvn37YmxszIkTJzh8+HCKH08IIf5t+fLl7Ny5EyMjIxYvXozBx85GZmLvZ7+sXLmSmzdvKpxGiJTz7E0Uu68+5/GrKKWjpIiQIH2WT323dEGTTm9wzfvxllMFnCywMpXWS+mRlZkhBZ0sPrmNQzY1Hj+FA7DyLxvi4z++XVSshltB4ckdUWRASSq+qNXqBF/HxMRw8eJFKleujJGRke52FxcXAgMDv3r/a9as4dmzZ8TGxvL06VM2btyYYG0ZlUqFr68vz58/Jzo6miNHjlCsWLEE+zAxMWHGjBmEhIQQGRnJ9u3bcXV1/eosQgghhBAi81mzZo2uxc64ceNYt24d7dq1w8XFJdWzODk50bFjRwDGjBmT6scXQmRez58/p2fPngD4+vomeF8u3ilbtize3t5oNBpdIUaIjCRereHMg1ccvhVMVGzGvNJfq4V5o22JeqtHvmIx1G358ZPqpkZ6FM9mlcrpRHIqns0Kc+NPX5jv3S4USxs1zx8acnBzlkS3u/4sjPDojxfphHgvScWXnDlzcuXKFd3X+/fvJzY2llq1aiXYLiwsDCsreVISQgghhBDpx4EDB2jTpg0APXr0YODAgQonejfz28DAgAMHDnDixAml4wghMoH37cbevHlDmTJl6N+/v9KR0ixfX18A1q5dy9WrV5UNI0QyComIwe9aIHeCMvb6Fgc2ZeHqGVOMjDX8OjwEvUTOzZdytcFQP0mnUkUaYaCvx3c5bD65jVkWLT92erfez8b5VrwN+3ivsji1lqO3XxIn7cfEJyTpGaNBgwbcuXOH3r17s23bNgYMGICenh7e3t4Jtrtw4YKuVZgQQgghhBBp3YULF2jUqBFxcXH4+PgwderUZF/HJSly5MhB27ZtAZn9IoRIHatWrWL79u0YGhqyZMkSaTf2CaVKleLHH39Eq9XK7BeRIWi1Wq4+DWXf9SDCohLpu5RBBD0xYNUMawB+6hqKc46Pf7/2FsbkzmqeislESnG1NcPF+tOtg2s0iCB7nlgiwvTZvDjxiQWhUXGcvBeS3BFFBpKk4ku/fv3IkycP06dPp1GjRty4cYNevXqRP39+3TanTp3i6dOnVK1aNdnCCiGEEEIIkVLu3buHp6cn4eHh1KhRg2XLlqGnl3aubhw0aBB6enrs3LmTCxcuKB1HCJGBBQYG0r17dwBGjBjxQZtv8SFfX19UKhUbNmzg0qVLSscR4pucf/Say09C0WiVTpKyNGqYO9qWmGg9CpeOxr3px9uNqVRQNuenZ0uI9KVsLlsM9BK/wErfAFr2eAPA3vUWBD5K/AKEx6+iuPo0NLkjigwiSe8mbW1tuXjxIosXL2b8+PHs37+fP//8M8E2gYGB9OzZk1atWiVLUCGEEEIIIVLKixcvqFOnDkFBQZQsWZLNmzdjbGysdKwE8uXLR7NmzQAYO3aswmmEEBmVVqulW7duvH79mtKlSzNgwAClI6ULxYoVw8fHB/hfGzIh0qMX4dHcCszYbcbe81trwa2LJpiYafjl9xASu+Ymn0MWbMyNPn6nSJeyGBtQxMXyk9uUqBBNiQpRqONVrP7b+pPbXnkaytM3UcmYUGQUSb6Uz9zcnLZt29K/f39q1qz5wf3e3t5MnTqVEiVKfFNAIYQQQgghUlJERAT16tXj7t275MqVi927d6fZdQuHDBkCwMaNG7lx44bCaYQQGdGxY8fYvn07BgYGLFmyBENDQ6UjpRsjRoxApVKxZcsWzp07p3QcIb5avFrDyXuvlI6RoqIjVTy9b8CZQ6asm2MNQMser3FwUX90e2MDPUpkT5uvC8W3KexsiYXJp1tqtuz5Gj19LWePmHH9XOIXZmm14H/3JWHRcckdU6Rz0rRVCCGEEEJkWnFxcTRp0oQzZ86QNWtW9uzZg7Ozs9KxElW0aFEaNWrE5s2bGTduHMuWLVM6khAiAwkKCmL+/PkA/P7773Ix5VcqXLgwLVq0YOXKlfj6+rJ9+3alIwnxVS49eUNEdPpd4yU+Dl4F6xMSZMCrIH1e/udzyAt93obpJ3hMiQpR1PB+m+g+S7paY2ygn+j9Iv3S11NRNpcNh24GJ7pN9tzx1PSOYP8mC1ZOt2HUksBEZ0jFqbUcu/0S96KOGOqnndbFQlnfXHy5du0ad+7cITw8HK32480g27Rp862HEUIIIYQQIllpNBo6dOjAnj17MDMzY8eOHRQoUEDpWJ81dOhQNm/ezKpVqxgxYgR58+ZVOpIQIgPQarV0796d8PBwSpYsyeDBg5WOlC4NHz6c1atXs2PHDk6fPs3333+vdCQhvsiL8GhuB6XddmMaDYS91iMk0ICQIH1CXhgQEvjvz/qEhuij1Sa+jsd7Zlk02DrGkzNfHC16vEaVyENszY3Ia2+ezN+JSEucrUzJYWvGo1eRiW7zY6dQ/tljzoPbRhzbZU61eokX60Kj4jgREELVAvYpEVekQ0kuvuzfv5+uXbsSEBCQ6DZarRaVSiXFFyGEEEIIkeYMHjyY5cuXo6+vz4YNGyhfvrzSkb5ImTJl8PDwwM/PjwkTJjBv3jylIwkhMoD169ezZcsW9PX1mT9/vrQbS6ICBQrQunVrli5dyogRI9i9e7fSkYT4rPftxhK5pjrFabUQGaF6N2PlhT4vAz+csfLqhQHxcZ8vrBgaabF1iMfOUY2d438/q7F1jMfM/Mu+0bK5bFAlVpkRGUaZnDY8C40iXv3xcWFpo6Fh+1BWz7Rh3RwryteKxMQ08TH05HUUV5+GUiybtKsTSSy+nD17lrp166JSqWjRogVXrlzhypUrDBo0iICAAPbv38/r169p3749OXLkSO7MQgghhBBCfJNp06YxceJEABYuXIinp6fCib7OsGHD8PPzY8mSJfz++++4uroqHUkIkY7duHGDLl26ANCkSRNKlSqlbKB0bvjw4axYsQI/Pz/8/f2pVKmS0pGE+KRLT0JTvN2YVgsRoXoEPjYg8IkBQY8N//+zAYFPDIkM/3ybJpWeFms7daKFFTvHeCxtNInOZPkaeezNyZol8TU+RMZhaqRPiexWnH/4JtFt6viEs3+TBcHPDNixwpImnUI/uc8rT0OxMTcim7VpMqcV6U2Sii/jxo0jPj4ePz8/3NzcaN++PVeuXGHMmDEAvHnzhs6dO7Njxw7Onj2brIGFEEIIIYT4FmvWrKF3794AjB8/nrZt2yqc6OtVrlyZ6tWrc/jwYf7880+mT5+udCQhRDp1//59ateuzatXryhXrhxNmjRROlK6lydPHtq3b8+CBQsYPnw4+/fvVzqSEIl6124sPFn2pdVC+Bs9gp4YEPjY8P8/G+i+joz4dIEli9X/F1Ec4rFz+vfndzNWbOzVGKTC6tWG+ipKuVqn/IFEmlHAwYJ7wW95Exn30fsNjaD5b6/5a4g9O1dYUMM7AjsHdaL702rB/+5L6hRzwtJEZpJmZkl6yvL396d06dK4ubl99H5ra2uWLVtGrly5GDZsGEuXLv2mkEIIIZQTHx9PZGTkBx9v377FxcWFggULKh1RCCG+2P79+3UtcXv06MGAAQMUTpR0Q4cO5fDhw8ybN48hQ4bg6OiodCQhRDrz/Plz3NzcePbsGUWKFGHbtm2cOnVK6VgZwtChQ1m6dCkHDhzgyJEjVKtWTelIQnwgXq3h1Fe2G9Nq36298rECS9CTzxdY7Bzjccwej5NrHI7Z3/87HnuX+E+2ckpNJbJbY2Kor3QMkYr09FSUzWXD/usvEt3m+xpRFCwZza1LJqybbU2XESGf3GecWsvR28HUKeqEof7nZ3aJjClJxZdXr14leOFgZGQEwNu3bzE3f7cQlbGxMVWqVGHfvn3JEFMIIcSnREZG8uzZs0SLJF9yW2K3x8bGJnpcfX199u3bR40aNVLxuxVCiKS5cOECjRo1Ii4uDh8fH6ZOnZqu+3jXqlWL8uXLc+rUKaZMmcKECROUjiSESEdevXqFu7s7AQEB5M6dm3379mFnZ6d0rAwjV65cdOjQgTlz5jB8+HAOHz6crv/miIzp0pNQwj/Sbux9geVdUcUwweyVoCcGRL39fIHFyTUex+xx///53dcOLvEYmaSNAktirM0MKeCYRekYQgEOFibksTfnXvDbj96vUkHLnm8Y/rMTx3eb4940nLxFEj9fAhAWFc+JgBCqFrBPicgiHUhS8cXe3p6wsLAEXwPcu3eP4sWL626PiooiNPTTPfCEEEJ8m+3bt9OqVasEz8spQaVSYWZmpvuIj4/n6dOnNGvWjPPnz5MtW7YUPb4QQnyLe/fu4enpSUREBDVq1GDZsmXo6aXvK9BUKhXDhg2jfv36zJo1i4EDB2Jra6t0LCFEOhAeHo6npydXr17F2dmZ/fv34+LiQlzcx9utiKQZMmQIixYt4ujRoxw6dIiaNWsqHUkIncTajd2/aci0Qfa8DEz8lKFKpcXWUY1T9ngcXeP+/3M8TtnTR4HlU8rmtJFCaSZWytWaJ6+jiI3XfPT+vEVi+cHzLcd3m7NyujW/z3nx2TWGnryO4urTUIpls0qBxCKtS1LxJV++fAQEBOi+/v7779FqtcydO5eZM2cCcPfuXQ4ePEiePHmSJ6kQQogEtFot06dPp0+fPmi1WszMzLCwsNAVR8zNzRMUS/79kZT7TExMErwIjYyMpFKlSly6dAkfHx8OHz6MoaH0MhVCpD0vXrygTp06BAUFUbJkSTZv3oyxccZYQLVu3bqULFmSS5cu8ddff+Hr66t0JCFEGhcdHY23tzenT5/Gzs6Offv2yfv2FOLq6sovv/zCzJkzGThwIMePH88wf39E+pZYu7Hg5/r82deBNyH6iRdYXOPeFVgy4FDOZWeGg6WJ0jGEgkwM9SnlasXp+68T3canyxtOHzTl1iUTTh8ypXzNqM/u9/KTUKzNDMluY5accVOF9mv6EooPJKn44uXlxaBBg7h69SrFihXDw8ODnDlzMnv2bM6cOUP27Nk5ePAg0dHRdOjQIbkzCyFEphcfH0/Pnj2ZNWsWgO5NXWoWP8zMzNiwYQNly5bF39+fAQMGMHXq1FQ7vhBCfImIiAjq1q3L3bt3yZUrF7t378bKKuNcdaZSqRg6dCg+Pj66grylpaXSsYQQaVRcXBw//fQThw4dwsLCAj8/P4oWLap0rAxt8ODBLF26lLNnz9K6dWvWrFmT7mdeivTv8tMP2429DVcxqY89b0L0cc0by++zgzC3zDwnXQ30VZTOYaN0DJEG5HOwICD4LSERH28pZuegpm6rcDYvtGLRBFus7YIpWPLT7ccATgSE4F7UECvT9HPR6s3AMLRqtdIx0rUk/cVv06YNs2fP1l0BbWRkxLZt2yhQoABnzpxh8+bNhIeH07FjR3r27JmsgYUQIrMLCwvTtZhRqVT8+eefzJkzR5FZJ/ny5WPZsmUATJs2jXXr1qV6BiGESExsbCw//vgjZ8+eJWvWrOzZswdnZ2elYyW7xo0bU6hQId68ecPs2bOVjiOESKM0Gg3t27dn27ZtmJiYsH37dsqWLat0rAzPxcWFTZs2YWhoyPr16+nVq5dcRSwUFRwew63AhO3G4uNg2iB7nt43wsY+nv5TgzNV4QWgqIslpkb6SscQaUS5XLafbCdWr1UYeQrHEBGqz7jujvjv/fyMlji1lmN3ghNtaZaWRMWqOXTzBecfvkEtf7O+SZKKL05OTnTu3DnBFTLFixfnxo0bXL9+nX/++Yfnz58zd+5c6ZMohBDJ6OHDh1SuXBk/Pz/MzMzYtGkTffv2VfS5tkGDBgwePBiAn3/+mRs3biiWRQgh3tNoNHTo0IG9e/diZmbGzp07KVCggNKxUoS+vr7ueXjy5MlERkYqnEgIkdZotVq6d+/OypUrMTAwYMOGDVSrVk3pWJlG7dq1dRcszZgxgwkTJiicSGRWao2Wk/dCErQb02phwThbrp8zwcRMQ7/Jwdg5ZK4r3S1NDSjsJDOHxf/YmhtRwDFLovebmGoZOusFZapGEher4u/hWdm8yPKDVn7/FRYVz4l7IcmcNnk9fhXJrivPeR4arXSUDOGrii9Xr15l7NixdOnShV69erFo0SLCwxNWywsVKkTFihWxt7dP1qBCCJHZnTlzhvLly+sWRj169CgNGzZUOhYAI0eOpGbNmrx9+5bGjRt/8LdBCCFS26BBg1ixYoXuJOP333+vdKQU1bx5c3Lnzk1wcDDz589XOo4QIo0ZOnSobtb0smXLqFu3rtKRMp1mzZrpWvQOHjyYJUuWKBtIZEqXnrz5oN3YpgVWHNuVBT19LT3GvCRXgTiF0imnTE4b9PTk4nGRUPFs1pgaJX7q3MRUS69xL/FqEQbAhnnWzBlpR9xnOpA9fR3FlSehyRk1WcSpNZy8F8KxOy+JSQezc9KLLy6+DBkyhFKlSvH7778zd+5cZsyYQadOnciXLx8nT55MyYxCCJHpbdy4kWrVqhEUFESJEiU4deoUZcqUUTqWjoGBAatXr8bFxYWbN2/SqVMnaacghFDM1KlTmTRpEgALFy7E09NT4UQpz9DQkEGDBgEwadIkYmJiFE4khEgrJkyYwLhx4wCYM2cOzZs3VzhR5tWrVy8GDBgAQMeOHdm1a5fCiURm8rF2Y0d2mLNp4bu18Nr3f0XJipnvSvfsNqY4W5kqHUOkQUYGepR2/fQ6QHr60LLHG9oPeIWevpbju80Z39OB8NBPn3K/8jSUJ6/Tzmz1lxEx7L4ayL3gt0pHyXC+qPiyadMmxo8fj0ajoXr16vTp04eOHTuSM2dOgoOD+fHHH4mOznxP0EIIkdK0Wi0TJ06kSZMmREVF4eXlxfHjx3F1dVU62gccHBxYv349BgYGrF27lr/++kvpSEKITGj16tX06dMHgPHjx9OmTRuFE6Wetm3bki1bNp4+fcrSpUuVjiOESAPmzp2rK8xOnDiRX375ReFEYty4cbRu3Rq1Wk3Tpk05deqU0pFEJqDWaDl1P2G7saunjVk4zhaABm1Dqdkw8510NdBT8V3OT59cF5lbrqzmOFoaf3a72o0j6D85GBMzDTcvmODb0ZHARwaffMyJgBBCo5SdaabVarnyJJT914OI+M+sOICA60a0bmRBUNDn17QRH/dFxZf3a7csWbKEAwcO8OeffzJ37lxu3LiBl5cXgYGBbN68OaWzCiFEphIbG0unTp0YOHAgAN27d2fr1q1YWFgonCxxlSpVYsqUKQD069ePf/75R+FEQojMZP/+/bRt2xaAnj176q4uziyMjY3p378/8K7wFB//4RsoIUTmsXr1arp06QK862Tx/vlBKEtPT4+FCxdSp04dIiMjqVu3Lrdu3VI6lsjgLj15Q1jU/14XPA4wZNpge9RqFRXd39K0c9prgZQaimazJIvxp0+QC1E2ly1f0pWuRIVofOcHYecUT+BjQ0Z0dOTGhcQLN3FqLcfuBBOrUIuv8Og49l0P4srTUDT/aVwS9VbF8qnWjOjoyMWzhixbVkSRjBnBFxVfzp8/T+nSpT+4ctDY2Jhx48ah1Wo5f/58igQUQojM6PXr13h6erJw4UL09PT466+/+OuvvzAwSPsvDH/77TeaNWtGfHw8Pj4+BAUFKR1JCJEJnD9/nkaNGhEXF4ePjw9TpkxBpcp8vbs7deqEvb099+/fZ/Xq1UrHEUIoZMeOHbRp0watVkvXrl0ZPXq00pHEvxgaGrJhwwbKli1LSEgIderU4dmzZ0rHEhnUy4iE7cZeB+szqY89UW/1KFgqms7DQtD7qhWh0z9naxPqFHWkqIuV0lFEOmBlakghZ8sv2tY1bxwjFwaSp0gMEWH6jOvuwLHdic8aCYuK58S9kOSK+sXuBUew+2ogLyM+XKDm/HETBrZwxm+tJVqNirqNYujU6XKqZ8wovujp9fXr1xQuXPij972//c2bN8kWSgghMrOAgAAqVqzIwYMHyZIlC9u3b6d79+5Kx/piKpWK+fPnU7hwYZ49e6YrxAghREoJCAjA09OTiIgIatasybJly9DLbGcR/p+ZmZmu7drYsWNRq9UKJxJCpLbDhw/TtGlT4uPjadWqFTNmzMiUxei0LkuWLOzcuZN8+fLx8OFDvLy8CA3NnLMPRMpRa7ScvPe/dmPRkSr+7GtPSJABzjnj6DPhJYZGymZMTY6WxrgVcaRGQQfssny+lZQQ7xVzscTcWP+LtrW20zBs1gvK1YhEHa9izh9Z2TDfisSWxX36OoorT1Ln+T8mXs3xOy85ee8V8eqEgV6/1OOvoXZM7udASJAB9s7xDJz2gvF/vcXa+sMijfgyX/SuVKPRYGho+NH73l+FLW/shBDi2/3zzz9UqFCBW7du4erqyj///IOXl5fSsb5alixZ2LRpE1myZOHw4cMMGzZM6UhCiAzqxYsXeHh48OLFC0qWLMnmzZsxNs7cb6a7du2KtbU1N2/eZNOmTUrHEUKkojNnzlC/fn2io6Px9vZm0aJFmbYYnR44ODiwZ88eHB0duXTpEg0bNiQmJkbpWCIDufyvdmPqeJgxLCsPbhthaaNmwJQXZLFSpt1RasuaxYhahR2oVdgRe4vM/TpRJI2Bvh7f5fjy9YGMTbT0GPOS+q3fFVU2L7Ti7xF2xCbyFH/laSiPX0UmR9REBYZGs/tKII/+cxyNBg5uMWdAMxdOHTBHT19L3ZZhjF/1nBIVZI33byWvwoQQIo1YtWoVNWvW5OXLl5QpU4ZTp05RokQJpWMlWaFChVi0aBEAEyZMYMuWLcoGEkJkOBEREXh5eXH37l1y5crF7t27sbT8spYAGZmlpSU9e/YEYMyYMWgTu8xOCJGhXLt2DQ8PD90swDVr1iR6EaVIO/LkycPu3buxsLDg8OHDtG7dWi5uFcniZUQMN/+/3ZhWC0sn23DR3xQjYw39/gzGIdvHx5mhvooq+bNSNpcNVqbp+znE1tyQ6gXtcS/qhKOlidJxRDrnamtGNhvTL95eTw+adQul4+AQ9PW1nNhrzrjuDoS9/vjp+BP3QgiNikuuuDpqjZbzj15z8OYLImMT/t4/vW/A6C4OLBxvR2SEHrkLxTBqUSAtur/BxFTeQySHLy6+bNiwgTx58nz0Q6VSJXp/3rx5UzK/EEKke1qtlpEjR9KyZUtiY2Np1KgRR44cwdnZWelo36xp06b07t0bgLZt23L37l2FEwkhMorY2Fh+/PFHzp07R9asWdmzZ0+GeN5MLj169CBLlixcunSJnTt3Kh1HCJHC7t27h5ubG69evaJ8+fJs2bIFExM50ZhelC5dms2bN2NoaMj69evp1auXFM7FN/lvu7EdKyw4sNkClUpLt5Eh5C368RZCVqaGeBRzwtXWjAKOFtQt4Uztwg7ksDX7ogXH0wprM0Oq5M+KRzFnXKy//GS5EJ9TJqcNBl/5y1DD+y0Dpr3ALIuG25dNGNHRkWcPPlzPN16t5ejtYGLjk29G2pvIWPZcC+Tm8/AEt8fFwsb5Vgxu7cytSyYYm2po1es1fywIIlfB5C8AZWZfXHyJiIjgwYMHH/3QarWfvF8IIcTHxcTE0KZNG0aMGAFA//792bBhA+bm5gonSz4TJkygcuXKhIWF8eOPPxIZmbJTaYUQGZ9Go6FTp07s3bsXMzMzdu7cSYECBZSOlabY2trStWtXAEaPHi0n8YTIwJ49e0bt2rV5/vw5xYoVY9euXVhYWCgdS3ylWrVqsWzZMgBmzpzJ+PHjFU4k0rN/txs7sc+MNX+/a5fUqtcbylaL+uhjctiaUaeoIxYmCWe7OFia8EP+rHiXykaJ7FaYGX3ZuhdKsDQ1oHI+O7yKO+Nqm/gi50IkVRZjA4q4fP1M+2LlYvCdH4i9SzwvnhoyopMT18592AIvPDoe/4CXyRGVW4Hh7LkWyJvIhMWUGxeMGdLamU0LrVDHqyhVOYoJq57j2Swc/Q9rQuIbfdGP9P79+ymdQwghMp2XL1/SqFEjjh8/jr6+PrNnz6ZTp05Kx0p2hoaGrFu3jtKlS3P58mV+/fVXli5dKgu/CiGSbNmyZWzZsgUDAwM2btzI999/r3SkNKlPnz789ddfnDp1ioMHD1KrVi2lIwkhkllISAhubm7cv3+fvHnzsnfvXmxtbZWOJZKoWbNmBAUF0atXL4YMGYKzszPt2rVTOpZIZ/7dbuzWRWPmjLQDwOOnMDx+Cv9ge5UKSmS3oqiL1Sf3a2qkT7FsVhR1seTJ6yjuvAgnMDRtrFGUxcSAYi6W5M5qLu8zRYor4mzJg5C3ugLnl8qWO54/FgQydaA9d64YM6GHAx0Gv6JavbcJtnv2JprLT95QIrt1kvJFxao5eS+E56EJ12t5G6Zi9d82HNqaBQArWzVt+rymfK1IPvZrY6CnoqCTBXmymhKQpCQCvrD4kjNnzpTOIYQQmcqtW7eoW7cuAQEBWFlZsWHDBmrXrq10rBTj4uLC2rVrqV27NsuXL6dy5cp07txZ6VhCiHRo2rRpujWkFi5ciIeHh7KB0jBHR0c6derEjBkzGD16tBRfhMhgwsLC8PDw4Pr162TLlo39+/dL+8UMoGfPnjx79oyJEyfSsWNH7O3tqVu3rtKxRDrx73Zjzx8ZMGVAVuLjVJStFknLHm8+2N7IQI/K+exwtvry1lwqlQpXWzNcbc0Ii47jTlAE94IjiFOn/ixbc2N9irpYkidrFvTSU180ka7p6akom9OWgzdffPVjrWw1DJkZxNxRdpzcb8680XYEPjagaedQ9P7Vn+rq0zBszIy+egbX41eRnL7/iph/tS7TauHUATOWTbEh9NW7mWs1vCNo3u015pYf/73NldWMktmtMTc2IC5O2pB9iy9uOyaEECJ5HDp0iAoVKhAQEECuXLnw9/fP0IWX96pXr864ceOAd2sRnDlzRuFEQoj0Zu3atQwYMACAsWPH0qZNG4UTpX39+/fH0NCQw4cPc/z4caXjCCGSSVRUFA0aNODs2bNkzZqVffv2kStXLqVjiWQyfvx42rRpg1qtpmnTppw6dUrpSCKduPI0lLCoeMJe6zGxtz0RYfrkLRJD1z9C0PtPtzAbs3fru3xN4eW/LE0MKZPThkals/F9bltszQ0//6BkYGqkR5mcNtQr4UI+BwspvIhU52RlQk67pLW2MzKGbiNDaNg+FIBtS62Y+bsdsdEJx/GJeyGERn1Z4SNereHUvRCO3XmZoPDyMlCfP/vZM2NYVkJf6eOcM45hs4PoOPjVRwsvDhbGeBRzolLerJgbSw+y5CDFFyGESEWLFy/G3d2dN2/eULFiRU6dOkWRIkWUjpVq+vXrR8OGDYmNjaVJkya8fJk8vUyFEBnfgwcP6NixIwD16tWjb9++CidKH1xdXXUta8aMGaNsGCFEsoiLi8PHx4cjR45gaWnJnj17KFy4sNKxRDJSqVQsWLAADw8PoqKiqFu3Lrdu3VI6lkjjQiJiuPE8jNhoFZP72/PiqSH2LvH0/TMYY5OEJ1lz2ZnhVsSRLMl0ctVAX498DlnwKOaMe1FHcmc1Rz8FzjgaG+hRytWa+iVcKOhkgb4UXYSCyuS0Ia990sa6nh407RxK599D0DfQcuqAOWN+cyD01f92Fq/WcvR2MLH/KqZ8zMuIGHZdDSQg+H/tyzRq2L3GggHNnbn4jyn6Bloadwhl3PLnFC79YbtACxMDquTPSu0ijtiaG339NyQSJcUXIYRIBRqNhiFDhvDzzz8THx9Ps2bNOHjwIA4ODkpHS1UqlYolS5aQL18+Hj16RMuWLVGr1UrHEkKkcRqNhvbt2xMREUGlSpVo37699PP+CgMHDkRPTw8/Pz/OnTundBwhxDdQq9W0adOGHTt2YGJiwo4dO/juu++UjiVSgKGhIevXr6dcuXKEhIRQp04dnj17pnQskUa9azf2CnU8zPK14+5VY8wt1QyY+gIr2/+duNVTwXc5ramULysGKVEdAbJmMaZiXju8S2WjdA5rsph8e4HHUF9FiexWNCjlQhEXyxTLLsTXMDHUp3weOxqUzEYRF0sM9b/+/UnVum8ZNP0F5pZq7l41ZkQHJ57c/9/vTHh0PP4BL9FqP5ylotVqufo0lP3Xg4iI/t/6Mw9uGzKioyMrptkQE6VHwZLRjFv+nB87hWL4n7qKkcG7WWR1izt/dYsz8WXk2UoIIVJYVFQUzZo107Xc+v3331m5ciUmJiYKJ1OGlZUVGzduxNTUlL179zJq1CilIwkh0ri///6bw4cPY2ZmxoIFC9DX1//8g4RO3rx5adGiBSCzX4RIz7RaLd26dWPNmjUYGhqyadMmqlSponQskYKyZMnCzp07yZ8/Pw8fPsTT05PQ0FClY4k06MrTUEKj4lg105ozh80wMNTSZ+JLXHL+74SssYEeNQo5UMjJMlUymRjqU9jZkgYlXahe0J5sNqYfXdT7Uwz0VRTLZol3qWwUy2aFoRRdRBpkaqRPKVdrGpZ+V3A0M/q69ypFysTgOz8Ix+xxBD83wLejE1dPG+vuf/YmmitPEz73h0fHse96EJefhKL5/7pMTLSKVTOs+b29E/duGGOWRUOHQSEMm/2CbLnjEzxeTwWFnC2oX9KZgk7Sui8lybOWEEKkoKCgIGrUqMH69esxNDRk6dKljBw5Ej29zP30W6JECebNmwfAyJEj2b17t8KJhBBp1e3btxk4cCAAEydOJF++fAonSp8GDx6MSqVi8+bNXL16Vek4QogkGDx4MHPnzkVPT4+VK1fi6empdCSRCuzt7dmzZw9OTk5cvnyZhg0bEh0drXQskYa8bze2d30Wdq9+V1jp/HsIhUr9r7WQrbkRHsWccLRU5gJAF2tTqhWwp0HJdzNXTAw//X7YQE9FIWcLGpR0oUR2a4wMMvf7Z5E+GOrr6QqOFfLYYmX65WsgueSM548FQRQsGU3UWz0m9Hbg4BZz3f1Xn4bx+FUkAPeCI9h9NZCXEbG6+y+fMmFgC2d2rrREo1ZRvtZbJq55Rs2Gb/nv6SdXW1PqlnDmuxw2GBvIRW0pTZ69hBAihTx69IgffviBU6dOYWtry/79+2Vx6H9p1aoVXbp0QavV0rJlSx48eKB0JCFEGqNWq2nXrh1RUVHUrFmTLl26KB0p3SpSpAiNGzcG0M3EFEKkH+PHj2fChAkAzJs3j6ZNmyqcSKSm3Llzs2vXLiwsLDh8+DCtW7eW1r0CAM3/txs7e8SUZVNtAPDp8oZK7pG6bXJnNcetiGOaWDzb3Njg3QyBUtmolNcOewvjBPfr60FBpyzUL+nCdzlsMDGUE8Mi/dHTU5HHPgt1SzhTraD9B+M8MRbWGgbPeEHlOm/RqFUsHG/HqhnWaP6/c+CJeyEcuR3MyXuviFe/m+4S+kqPWSPsmNDTgeBnBtg5xtN3UjA9xoRgkzXhWjF2WYyoXcSBKvntsTD58sKQ+DZSfBFCiBSwd+9eBg0axMOHD8mfPz8nT56katWqSsdKc6ZOnUq5cuV4/fo1TZo0kav4hBAJTJkyhRMnTmBhYcGiRYsy/azBbzV06FAA1qxZw507dxROI4T4UrNnz2bw4MEATJ48mQ4dOiicSCihdOnSbNmyBUNDQzZs2ECvXr0+ugaAyFyuPA3l/DkVM3+3Q6tRUcM7ggZtwoB3bYXK5rKhYl67NLcwvZ6eilz/XxTyKu5EPocs5HPIQr0SLpTJaYvpV7ZtEiKtymZtilsRR9yKOJL9C1rvGRpBF98QGnd8A8DOlZb8NSQrMdEq4tVanr6OAkCrhSM7zBnQzJl/9pijUmnx+CmMCaue812VqAT7NDfWp1JeO+oUdcLBInO2v1dSkt7Brlq1itjY2M9vKIQQmdCcOXPw9vYmMjKSKlWqcOLECfLnz690rDTJ2NiYDRs2YGdnx7lz5+jZs6fSkYQQacS1a9cYNmwY8K5QmzNnToUTpX+lS5embt26aDQaxo8fr3QcIcQXWLFiBd26dQPerRvYp08fhRMJJdWsWZPly5ejUqmYOXOmPJdnciERMRw+95Y/+9oTG6NHiQpRtO//CpUKTI30qFnYgQKOFkrH/CxrMyO+z23L97lt08TsHCFSgr2FMVUL2ONV3Jm89uZ8avkilQp+7BhGV9+XGBhqOXPYjNFdHXgT8u5BgY8MGNfdgXmj7YgI0ydH/lj+WBBE695vMDX/X1HeUF9FSVcr6pVwIVdW88QOJ1JYkoovrVq1wsXFhd69e0vPaCGE+JdZs2bRpUsX1Go1NWrUYNeuXdjZ2SkdK03LkSMHq1atQqVSMW/ePJYsWaJ0JCGEwuLi4mjbti2xsbF4eXnx888/Kx0pw3g/+2XZsmU8evRI4TRCiE/Ztm0b7dq1Q6vV0r17d/744w+lI4k04KeffmLatGkADBkyhMWLFysbKBPq0AHad4lm+94Y4uOVmX2k0WjZf+k1E3s7EPb63cnXHmNeom/wrrWQR1FnucJdiDTIytSQ8nnsaFAyG0VcLDHUT3wqTGWPSAbPeEEWKzX3rhszvIMTa2ZZMaiVM9fOmmBorKFZt9eMWhxI3qL/myShUkF+x3ft+4q6WKW5mW+ZTZKKL23btiUmJobp06dTsmRJKleuzJIlS4iMjPz8g4UQIoO6dOmS7mrEwYMH06NHD4yNv6y3Z2bn7u6uO6HQpUsXLl68qGwgIYSixo0bx7lz57CxsWH+/PmoPjc/X3yxihUrUrNmTeLj45k4caLScYQQHxEUFMSgQYPw8fFBrVbTtm1bpk2bJs+FQqdHjx4MHDgQgE6dOrFz506FE2Uer17B0qWwZI4JDeoYY5tVQ90fY1iwLIbQ0NQrxJy7F8of3a159sAQW4d4+k8OxtRcS157c9wKO0rbLiHSOFMj/XfrH5XORukc1pgl8jtbqFQMfywIwjlHHCGBBmxfZkVcrIpi30cxYWUg9VuHY/CvCWPO1iZ4FXOmXC5bWTMpjUhS8WXx4sU8e/aMWbNmUbp0aU6cOEGHDh1wcXGha9eunD9/PrlzCiFEmvb27VuaNWtGTEwM9erVw9fXV94gf6WhQ4fi6elJdHQ0P/74I2/evFE6khBCARcuXGDUqFEAzJw5ExcXF4UTZTzv27ktWLCA58+fK5xGCPHegwcP6NatG7ly5WLChAnExMTQpEkTFixYIGteiQ+MGzeONm3aoFaradq0KSdPnlQ6UqZgagqrVkEd7xjMLTWEh+qza5Mxndoak9UeKlSNZcykWO4/0Hx+Z0kUEhFL7+6G3LhggomZhv6Tg8nqqOb73LaUz2OHnlzlLkS6YaivR2FnSxqUdKFCHlusTA0/2MbJNR7f+UGUrBiFjX08XUa8ZND0YByzx+u2sTYzpEYhe2oUdMDK7MN9COUk+RWchYUFv/76K2fPnuX8+fN07twZlUrFnDlzKFeuHN999x1z584lPDw8OfMKIUSa1KtXL27evImzszOLFy+WwksS6OnpsWLFCnLlysW9e/do06YNGk3KvWkRQqQ9MTExtGnThvj4eBo3bkzz5s2VjpQhVa9enYoVKxITE8OUKVOUjiNEpnfjxg3atm1Lvnz5mDVrFtHR0VSoUIFt27axdu1aDAxkDQTxIZVKxYIFC/D09CQqKoq6dety8+ZNpWNleKam4OMDfluMef1SxZZdsbT5JRqXHPHEx6k4dcyIYQOMyJNbj/xF4ujRP5YTJzUk19sajUbLb31j+MfPHH19Lb3GvaRQUQ21iziSzyFL8hxECJHq9PRU5LHPQt0SzlQraI+9RcIuKlmsNAyYGsyMbc/4wTOS96ecTI30+D63LZ7FnHC2MlUguficZLl8plSpUsyaNYvnz5+zZMkSKlWqxMWLF+natSvOzs506NCBc+fOJcehhBAizVm7di0LFixApVKxcuVKsmbNqnSkdMvW1pYNGzZgbGzM9u3bmTBhgtKRhBCp6I8//uDq1avY29sze/ZsKWSnEJVKpZv9Mnv2bF6+fKlwIiEyp7Nnz/Ljjz9StGhRli1bhlqtxs3NjUOHDuHv70/9+vVlxov4JENDQ9avX0+5cuV49eoVHh4ePHv2TOlYmYahoQpvTyOWzjXhyQMDLlxSM3B4DCXKxKHS03L3hiEz/jSiUkU9HJzVNG8by+ataqKikn7MUVOiWDPPAoCfB72iZm0tHsWcyJpF2l0LkVFkszbFrYgj7kUdyW5jyr/fEr3/t4GeimLZLKlXwoV8DlnkfVMalqyv5NRqNbGxscTExACg1WqJiYlh8eLFfP/99zRt2pSwsLDkPKQQQijq/v37/PLLL8C7BS9r1KihcKL0r0yZMsycORN41xrnwIEDCicSQqSGkydP6gquc+bMwcHBQeFEGZunpyelS5fm7du3TJ8+Xek4QmQaWq2Ww4cP4+7uTrly5di0aRNarZZGjRpx+vRp9u7dS/Xq1eUkivhi5ubm7Ny5k/z58/Pw4UM8PDykfa8CVCooVUKf8X8Yc+msIYHPYcacWGp5xmJqpiHkhT5rlhnRuKE+NnZaanvGMXuumqCgLz/Gxm1xjBz07sr2hu1D+aWjilqFHGRdByEyqKxZjKlawJ66JZzJa2+O/v+fxc+d1Zx6JZ0pkd0aQ325SCOtS5b/odOnT9OpUyecnZ3p3LkzFy5cwNvbm127dhEWFsaKFSsoXrw4mzZtonv37slxSCGEUFxcXBwtWrQgLCyMihUrMmLECKUjZRgdOnSgffv2aDQamjdvzpMnT5SOJIRIQZGRkbRt2xaNRkOLFi1o3Lix0pEyPJVKxdChQwGYMWMGoaGhCicSImPTarXs2LGDypUrU6NGDfbt24e+vj5t2rTh2rVrbNq0iXLlyikdU6RT9vb27NmzBycnJ65cuULDhg2Jjo5WOlam5uCg4rfORuzfZcTrV3ps3a6mZftY7J3UxESpOOBnSNdf9XF21lKqbDwjRqq5dg202o/v78IFLa1b6KNRq/jB8y1/jtenbC5bWd9FiEzA0sSQ8nnsaFAyG17FnaiY1w4zI2lJml4kufjy5s0bZsyYQcmSJalYsSILFy7E2tqaESNG8PDhQzZv3oyHhwempqa0aNGCs2fPUqRIEXbu3Jmc+YUQQjG+vr6cPHkSKysrVq1ahaGhLGqWXFQqFX///TelSpUiODiYpk2bEhsbq3QsIUQKGTp0KLdv38bZ2ZkZM2YoHSfTaNSoEYULFyY0NJS///5b6ThCZEjx8fGsXr2aUqVKUb9+fU6cOIGxsTFdu3bl7t27LF26lCJFiigdU2QAuXPnZvfu3VhYWHDkyBFat26NWq1WOpYAjI2hQT19ViwyIuiZPufOaRkwJI7CxePRalVcOmfAyBH6FCsGOXKp+bVbPAcPQlzcu8c/fQoeXlqi3upRrGwM61caklfWdxEi0zE10sfazEjpGOIrJan40rp1a1xcXOjVqxfXrl3Dw8ODLVu28ODBA4YPH46Li8sHjzEwMKBcuXK8fv36m0MLIYTSDh48yLhx4wCYP38+uXLlUjZQBmRqasrGjRuxtrbm5MmT9OvXT+lIQogUcPToUV3bqwULFmBra6twosxDT09PN/tlypQpvH37VuFEQmQcMTExzJ8/n0KFCtGiRQsuX76MhYUFAwYM4MGDB/z999/y+lEku1KlSrFlyxaMjIzYsGEDPXv2RJvYVAqhCJUKvvtOxYQxhly/bMCTJ/DXTDXVa8djZKTlySN95s4yoFYtsLXT0KiJGvc6Gl4E6pEjTzx7dxrgZCMnX4UQIr1IUvFl5cqVWFtbM3jwYAICAti5cycNGjT47GKAjRo1Yvjw4UkKKoQQaUVwcDCtWrVCq9XSqVMnmjZtqnSkDCtPnjwsW7YMeNcWZ/Xq1QonEkIkp4iICNq1a4dWq6VDhw54eXkpHSnT+emnn8iTJw8hISHMmzdP6ThCfJZGo+HXX3+lUqVKdOvWjSVLlnD16tU0c4X/27dvmTp1Knny5OGXX34hICAAOzs7Ro0axcOHD5kwYQJOTk5KxxQZWM2aNVm+fLluJnm3bt3SzO9HSli9ejV37txROkaSZcsG3bvpc2ifAa9eqdi4UUvzVvHY2GmICNdjy0Z9rl/Twy6rhsP79XF2kPVdhBAiPUlS8WXdunU8evSI0aNHkzNnzi9+XP369b96TYRx48ahUqno1auX7jatVouvry8uLi6YmppSvXp1rl27luBxMTExdO/enaxZs2Jubk6DBg1kzQAhxDfTarW0b9+e58+fU7hwYaZNm6Z0pAyvfv36DBkyBICOHTty9epVhRMJIZJL//79uX//Pjly5GDKlClKx8mUDAwMGDx4MACTJk2StV9EmvfHH38wd+5cTpw4waxZs2jfvj3FixfH0tKSKlWq0KdPH1atWsWdO3fQaDSpluv169eMGjWKnDlz0qdPH549e0a2bNmYOnUqDx8+ZNiwYdjY2KRaHpG5+fj4MGfOHFQqFbNnz6Zly5YZroWvVqtlxIgRtGjRAm9v7wwxe9PcHBo3VrFquQHBQXr4+0O//hpq11Gzb68euXPL+i5CCJHeJGl1niZNmiR3jo86c+YM8+bNo0SJEglunzhxIlOmTGHJkiUUKFCA0aNH4+bmxq1bt7CwsACgV69ebN++nTVr1mBnZ0ffvn2pV68e586dQ19frhQQQiTNX3/9xc6dOzE2NmbNmjWYmZkpHSlTGDlyJKdOneLAgQN4eXlx4sQJsmXLpnQsIcQ32Lt3L3PmzAFg0aJFWFpaKpwo82rTpg2jRo3i0aNHVKtWjV27dn20jbAQStu+fTsjR44E3q0VFR0dzdmzZzl37hwREREcP36c48eP67a3srKiTJkylCtXjrJly1KuXDly5MiBSpV8JzCfP3/O1KlTmT17NhEREQDky5ePgQMH0rp1a4yNjZPtWEJ8jV9++QVra2tatWrF2rVrefPmDRs3bsTc3FzpaN8sOjqan3/+WTcrvmHDhpiamiqcKnnp60PFilCxYpKXahZCCJEGJKn4khoiIiJo2bIl8+fPZ/To0brbtVot06ZNY+jQoTRu3BiApUuX4ujoyKpVq+jcuTOhoaEsXLiQ5cuXU7t2bQBWrFiBq6sr+/fvp06dOop8T0KI9O3ChQsMGDAAgMmTJ39QGBYpR19fn7Vr11K5cmVu3bqFl5cXx44dk5O1QqRTb968oUOHDgB069aNWrVqKZwoczMyMmLLli14enpy6dIlKlWqhJ+fH4UKFVI6mhA6d+7coXXr1gD89ttvCd4jqtVqbt++zZkzZzh79ixnzpzh4sWLhIaGcvDgQQ4ePKjb1t7enrJly+qKMWXLlsXZ2fmr89y/f59JkyaxaNEiYmJiAChRogRDhgyhSZMmcsGfSBN8fHywsrKicePG7Nmzh9q1a7Nz5850vb5acHAwDRs2xN/fHwMDA+bMmUObNm3YtWuX0tGEEEKID3xR8SVPnjxJPoBKpSIgIOCrH9etWzfq1q1L7dq1E7ywvn//PoGBgbi7u+tuMzY2plq1avj7+9O5c2fOnTtHXFxcgm1cXFwoVqwY/v7+iRZfYmJidC+cAcLCwgCIi4sjLi7uq78Hkfze/z/I/4dIbREREfz000/ExsZSv359OnXq9MlxKGM1+VlaWrJt2zaqVq3K5cuXadSoEdu2bcPISBacTA4yZkVq6tmzJ0+ePCFv3ryMHj36q8adjNWUUaxYMY4cOUK9evW4e/culStXZvPmzVSsWFHpaBmCjNtvExERQaNGjQgNDaVSpUqMHz/+g59lvnz5yJcvH82bNwfe/ayvXbvG+fPndbNjrly5QnBwMLt372b37t26x2bLlo3vvvuOMmXKULZsWcqUKYOdnd1Hs1y/fp1JkyaxZs0a3ToaFSpUYNCgQXh6eqJSqdBoNKna8iw5yVjNeGrWrMmePXto0KABJ0+epEqVKuzcuTNdziK/ceMGDRs25P79+1hbW7N27Vpq1Kgh41akKzJeRXojY/bjvvTnodJqtdrPbaSn923THL/2heeaNWsYM2YMZ86cwcTEhOrVq1OqVCmmTZuGv78/lStX5unTpwnaIfzyyy88fPiQPXv2sGrVKtq3b5+gkALg7u5O7ty5mTt37keP6+vryx9//PHB7atWrZLWQkJkcjNmzODAgQPY2dkxdepUmXGhoLt37zJs2DCio6OpVq0avXr1Stb2IUKIlHX69GnGjh2LSqVi7NixFC5cWOlI4l/CwsIYPXo0t2/fxsjIiD59+lChQgWlY4lMTKvVMnnyZI4fP46NjQ2TJ09O8lX7sbGxPHjwgLt37+o+njx58tH3q46OjuTNm5f8+fOTL18+DAwM2LJlC6dOndJtU6pUKZo0aULRokXltYhI8x49eoSvry+vXr3CwcFBt45uenHp0iUmTJhAZGQkjo6O/P7772TPnl3pWEIIITKpyMhIWrRoQWho6CfPEX7RzJfUvGrn8ePH9OzZk71792JiYpLodv99cavVaj/7gvdz2wwePJg+ffrovg4LC8PV1RV3d3c50ZpGxMXFsW/fPtzc3DA0NFQ6jsgk1qxZw4EDB9DT02Pt2rVUrVr1s4+RsZqyChQoQKNGjThy5AgVKlRg1KhRSkdK92TMitQQEhJC586dAejduzd9+/b96n3IWE15DRo0oEWLFuzatYuJEyfy119/8csvvygdK12TcZt006dP5/jx4xgYGLB582YqVaqUrPuPiIjg4sWLnDt3TjdD5u7duwQFBREUFIS/v3+C7VUqFQ0bNmTgwIF89913yZolLZCxmrF5eHjg5eXF3bt38fX1Zfv27ZQuXVrpWJ+1ePFiRo0aRXx8PBUrVmTDhg3Y29vr7pdxK9ITGa8ivZEx+3HvO2Z9Tppb8+XcuXO8ePGCMmXK6G5Tq9UcPXqUmTNncuvWLQACAwMT9OZ98eIFjo6OADg5OREbG8vr16+xsbFJsM2nXqwbGxt/dEFEQ0NDGVxpjPyfiNRy7949unXrBsCwYcO+el0CGaspo379+sybN48OHTowYcIEcuXKxa+//qp0rAxBxqxISb169SIoKIjChQszZsyYbxprMlZTjpWVFVu3bqVLly4sWLCA3377jcDAQEaOHClX938jGbdf5/DhwwwaNAiAqVOnUq1atWQ/ho2NDTVq1KBGjRq6216/fs358+cTrCETFBREs2bNGDhwYKaYsSdjNWPKnz8/x48fx8PDg4sXL+Lm5sb27du/6OIyJWg0GoYMGcKECRMAaN68OYsWLUr0Ql0ZtyI9kfEq0hsZswl96c/i2/qJpYBatWpx5coVLl68qPsoW7YsLVu25OLFi+TJkwcnJyf27dune0xsbCxHjhzRFVbKlCmDoaFhgm2eP3/O1atXk/1KKSFExhUXF0fz5s0JDw+ncuXK/P7770pHEv/y888/4+vrC7xbJ2zbtm3KBhJCfNL69etZu3Yt+vr6LF269JMznIXyDAwMmDdvnu55dvTo0XTo0EF6PYtU8+TJE3x8fFCr1bRu3Vp3MUxqsLGxoVatWgwaNIgNGzbw8OFDoqKiWLJkSaYovIiMzdHRkcOHD1O1alXCwsKoU6cO27dvVzrWByIjI/Hx8dEVXkaMGMHKlSvl9YMQQoh05Ztnvjx69Ijnz59/sL7Kv33NVRQWFhYUK1YswW3m5ubY2dnpbu/Vqxdjx44lf/785M+fn7Fjx2JmZkaLFi2Ad1frdejQgb59+2JnZ4etrS39+vWjePHi1K5dOwnfpRAiM/r99985ffo01tbWrFy5EgODNDdZMNMbPnw4jx8/ZuHChTRr1oyDBw/K2gRCpEFBQUF06dIFeNfmtVy5cgonEl9CpVIxYsQIXFxc+PXXX1m8eDGBgYGsW7eOLFmyKB1PZGAxMTE0adKE4OBgSpYsyZw5cxSfdaX08YVITlZWVvj5+dGsWTO2bdtGo0aNWLRoEW3atFE6GvDu4llvb2/OnDmDkZERixYtomXLlkrHEkIIIb5aks8kLlq0iFGjRvHo0aPPbqtWq5N6mI8aMGAAUVFRdO3aldevX1O+fHn27t2LhYWFbpupU6diYGCAj48PUVFR1KpViyVLlqCvr5+sWYQQGdO+fft0V1ktWLCAnDlzKpxIfIxKpWL27Nk8e/aM3bt3U79+ffz9/cmfP7/S0YQQ/0+r1dK5c2dCQkIoWbKkzCJMhzp16oSzszM+Pj7s3r2bGjVqsHPnThwcHJSOJjKonj17curUKWxsbNi0aRNmZmZKRxIiwzE1NWXjxo107NiRpUuX0rZtW0JCQujdu7eiua5cuULdunV5/PgxdnZ2bN68mSpVqiiaSQghhEiqJBVfFi9eTMeOHQEoXrw4BQoUSNGr3w4fPpzga5VKha+vr64NwseYmJgwY8YMZsyYkWK5hBAZ04sXL3RXfXXu3Jkff/xR4UTiUwwNDVm3bh3Vq1fn3LlzeHp64u/vLycFhUgjVqxYwdatWzE0NGTZsmUYGRkpHUkkQb169Th06BB169bl7NmzVKpUCT8/P/Lly6d0NJHBLFq0iLlz56JSqVi1ahV58uRROpIQGZaBgQGLFi3Czs6OKVOm0KdPH0JCQhg1apQis712797NTz/9RHh4OAUKFGDnzp3yd0YIIUS6lqTiy5QpUzAwMGDjxo3Ur18/uTMJIYRiNBoN7dq1IzAwkKJFizJ16lSlI4kvkCVLFnbu3EnFihUJCAjQnSQ0NzdXOpoQmdqTJ0/o3r07AL6+vpQoUULhROJblC9fHn9/f+rUqUNAQACVKlVi165dlC1bVuloIoM4e/YsXbt2BWDkyJF4eHgonEiIjE9PT48///yTrFmzMmTIEMaMGcPLly/5+++/U7VzyN9//02PHj3QaDRUr16djRs3Ymtrm2rHF0IIIVKCXlIedOfOHapWrSqFFyFEhjN9+nR2796NiYkJa9aswdTUVOlI4gs5Ojri5+eHnZ0dZ86c4aeffiI+Pl7pWEJkWlqtlo4dOxIaGsr333/PgAEDlI4kkkGBAgU4ceIEpUuXJjg4mOrVq7N7926lY4kMIDg4mMaNGxMTE0ODBg0YMmSI0pGEyDRUKhWDBw/WzTqbO3cuzZs3/+TavslFrVbTq1cvfvvtNzQaDe3bt2fPnj1SeBFCCJEhJKn4YmtrK4tsCiEynHPnzjFw4EDg3bpRxYoVUziR+FoFChRg27ZtmJiYsHPnTrp27YpWq1U6lhCZ0vz589mzZw8mJiYsXboUA4MkLzUo0hgnJyeOHDmCm5sbb9++pX79+ixZskTpWCIdi4+Pp3nz5jx+/Jj8+fOzbNky9PSS9FZVCPENfvnlF9atW4eRkRHr16+nfv36REREpNjxIiIiaNiwIdOnTwdg3LhxLFy4UFqUCiGEyDCS9IrW29ub06dPExUVldx5hBBCEeHh4TRr1oy4uDgaNWpE586dlY4kkqhSpUqsWrUKlUrF/PnzGTt2rNKRhMh07t+/T9++fQEYM2YMhQoVUjiRSG4WFhbs2LGDVq1aoVarad++PWPGjJGCt0iSYcOGceDAAczNzdm8eTNWVlZKRxIi02rSpAk7d+7E3Nycffv2Ubt2bUJCQpL9OE+ePKFKlSrs2LEDExMT1q1bx6BBgxRZa0YIIYRIKUkqvowdOxZLS0vatWvHmzdvkjmSEEKkvt9++427d+/i6urKggUL5EV/OteoUSNmzJgBvDuhs3TpUoUTCZF5vG8ZEhERQZUqVejZs6fSkUQKMTIyYtmyZQwaNAh493zbrVs31Gq1wslEerJx40YmTJgAwKJFiyhatKjCiYQQtWvX5uDBg9ja2nLq1CmqVKnCkydPkm3/586d4/vvv+fixYs4ODhw+PBhmjZtmmz7F0IIIdKKL+r/8PPPP39wW+HChdmwYQN79+6lbNmyZM+e/aMnK1UqFQsXLvz2pEIIkUJWrFiha2+xcuVK6S+cQXTr1o1Hjx4xceJEOnbsiLOzM+7u7krHEiLDmzlzJkeOHMHMzIzFixen6mK9IvWpVCrGjRtHtmzZ6NGjB7Nnz+b58+esWrVK1k0Tn3Xjxg3atWsHQL9+/fDx8VE2kBBC5/vvv+f48eO4ublx48YNKleuzL59+yhQoMA37Xfr1q20aNGCyMhIihYtyo4dO8iVK1fyhBZCCCHSmC8qvnyqh3NoaCgHDhxI9H4pvggh0rKAgAC6dOkCwPDhw6lSpYrCiURyGjduHE+ePGHVqlX8+OOPHD16lNKlSysdS4gM6/bt27pZEH/++Sd58+ZVOJFILb/99htOTk60atWKLVu2ULt2bbZv3y4XNIhEhYWF0ahRIyIiIqhRowbjxo1TOpIQ4j8KFy7MP//8g7u7O7dv3+aHH37Az8+P77777qv3pdVqmTp1Kv369UOr1eLu7s66deukzaAQQogM7YuKL4cOHUrpHEIIkepiY2Np1qwZERERVK1alWHDhikdSSQzPT09Fi1axPPnzzl06BBeXl6cPHmSnDlzKh1NiAxHrVbTrl07oqKiqF27Nr/++qvSkUQqa9KkCQ4ODnh7e+Pv70/lypXx8/OT51zxAY1GQ9u2bbl16xbZs2dnzZo1GBh80VtTIUQqy5kzJ8ePH8fDw4Pz589TvXp1tm3bRvXq1b94H3FxcXTv3p25c+cC8OuvvzJjxgz5vRdCCJHhfdFfumrVqqV0DiGESHXDhg3j7Nmz2NjYsGLFCmmNk0EZGxuzefNmqlSpwpUrV/Dw8OCff/6Rq7GFSGaTJ0/mxIkTWFpasnDhQlk7K5OqWrWq7iTdzZs3qVixIrt376ZkyZJKRxNpyIQJE9iyZQtGRkZs3LgRBwcHpSMJIT7B3t6eQ4cO4e3tzeHDh/Hw8GDt2rV4e3t/9rGhoaH4+Piwd+9eVCoVU6ZMoWfPnvI6QQghRKagl5QHLVu2DH9//89ud/LkSZYtW5aUQwghRIras2cPkyZNAt4t7urq6qpwIpGSrKys2LVrF9mzZ+fmzZt4e3sTHR2tdCwhMoyrV6/y+++/AzBt2jRy5MihcCKhpKJFi3LixAmKFSvG8+fPqVKlCgcPHlQ6lkgj9u7dq5ttPHPmTL7//nuFEwkhvoSlpSW7d++mYcOGxMTE0Lhx40+2qAd48OABlSpVYu/evZiZmbFlyxZ69eolhRchhBCZRpKKL+3atWPBggWf3W7hwoW0b98+KYcQQogUExQURJs2bQDo2rUrDRs2VDaQSBXZs2dn9+7dWFlZcfz4cVq3bo1Go1E6lhDpXlxcHG3btiU2NpZ69erpFs8WmVv27Nk5duwYVatWJTw8HA8PD1avXq10LKGwBw8e0Lx5czQaDR07dqRTp05KRxJCfAUTExPWr1/Pzz//jEajoX379kyePPmj2548eZLy5ctz/fp1XFxcOHbsGA0aNEjlxEIIIYSyklR8+VIajUauaBBCpCkajYY2bdrw4sULihcvzp9//ql0JJGKihUrxubNmzE0NGTDhg307dtX6UhCpHtjx47l/Pnz2NjYMG/ePHntJ3Ssra3Zs2cPTZs2JS4ujhYtWiR6kk5kfFFRUTRu3JhXr15Rrlw5ZsyYoXQkIUQSGBgYsGDBAvr37w9Av379GDx4MFqtVrfNunXrqFGjBi9evKBUqVKcOnWK7777TqnIQgghhGJStPhy7949LC0tU/IQQgjxVaZMmcLevXsxNTVlzZo1mJqaKh1JpLIaNWqwdOlS4F17pClTpiicSIj06/z584wePRqAWbNm4ezsrHAikdaYmJiwZs0aevToAbw7SdenTx+ZeZjJaLVaunTpwoULF8iaNSsbN27ExMRE6VhCiCRSqVRMnDiRCRMmADB+/Hg6d+5MfHw8Y8eO5aeffiI6Opr69etz7NgxsmfPrnBiIYQQQhkGX7rhyJEjE3x98eLFD257Lz4+nlu3bnH06FHc3Ny+LaEQQiSTM2fOMHjwYODdSfciRYoonEgopXnz5jx58oQBAwbQt29fsmXLxk8//aR0LJGBREREcP78ecqUKYO5ubnScZKVVqvlwoUL7Nixg4ULFxIfH0+TJk3kd0gkSk9Pj2nTppE9e3YGDBjA1KlTefbsGUuXLsXY2FjpeCIVzJkzh6VLl6Knp8fatWtlrT0hMogBAwZga2tL586dmT9/PgcOHODevXsA9OrViz///BN9fX2FUwohhBDK+eLii6+vLyqVCq1Wi0ql4uLFi1y8ePGTj3FwcGDs2LHfmlEIIb5ZWFgYzZs3150klB7jol+/fjx69IiZM2fSpk0bnJ2dqVq1qtKxRAbw4MEDvLy8uHHjBkZGRlSrVg0vLy88PT0pUKBAumzLFRkZyf79+9mxYwc7d+7k2bNnuvuyZcvGrFmz0uX3JVKPSqWif//+uLi40K5dO9auXUtQUBBbtmzByspK6XgZjkaj4e3bt1hYWCgdhRMnTtCzZ08AJkyYQM2aNRVOJIRITh07dsTW1pbmzZtz79499PX1mTFjBl26dFE6mhBCCKG4Ly6+LF68GHh3tePPP//MDz/8QIcOHT66rZGRES4uLlSoUEGuZhNCpAndunUjICCAHDlyyJoEAnh3InDatGk8ffqUzZs34+3tzfHjxylatKjS0UQ6dvbsWerVq0dQUBCGhobExsayb98+9u3bR+/evcmdO7euEFOjRg3MzMyUjpyoR48esXPnTnbs2MHBgweJjo7W3Wdubo67uzv16tWjYcOG2NraKphUpCctW7bEwcGBxo0bc/jwYapUqYKfnx8uLi5KR8sQoqKiWLZsGZMnT+bOnTtUrlyZTp060bRpU0WebwIDA2nSpAlxcXE0bdpU1loTIoNq3Lgxe/bsYerUqXTr1g13d3elIwkhhBBpwhcXX9q2bav799KlS/H09ExwmxBCpFXLli1jxYoV6Ovrs3r1amxsbJSOJNIIfX19Vq5cSe3atfH398fT05OTJ0/KSUCRJNu3b6dZs2ZERkZSsmRJdu7cSUREBLt27WL37t0cOXKE+/fv8/fff/P3339jbGxM9erVdcWY/PnzK5pfrVZz5swZtm/fzo4dO7h8+XKC+3PlykW9evWoV68e1apVk/UaRJK5ublx9OhRvLy8uHLlCjVq1ODQoUPy3PsNQkJCmDVrFjNmzCA4OFh3+z///MM///xDz549adWqFZ06daJkyZKpkikuLg4fHx+ePXtGkSJFWLhwoVz8IkQGVr16dapXr650DCGEECJN+eLiy78dOnQouXMIIVLZlStX6N69O9bW1hQpUkT3UahQoTR9JfbXiIuL4/z583Tt2hV41z6xUqVKCqcSaY2pqSnbtm2jUqVK3L59G09PT44dO4alpaXS0UQ6MmvWLLp3745Go6FOnTqsW7dON4YKFixI7969iYiI4NChQ7pizMOHD9mzZw979uyhZ8+e5M2bV1eIqV69OqampimeOywsjL1797Jjxw527dqV4KStnp4elSpV0hVcihQpIidORbIpXbo0/v7+VK9endu3b1OjRg0OHz6Ms7Oz0tHSlXv37jFlyhQWLVpEVFQUADlz5qR37954eXmxbt06FixYwIMHD3SF33LlytGpUyeaNWuWom3J+vfvr/t7umnTpjTRAk0IIYQQQojUlKTiixAifbt37x7u7u4EBgYCsHXrVt19KpWKXLlyJSjIFClShMKFC6fJN82RkZHcu3ePu3fvEhAQkODzo0ePUKvVwLsrsQYPHqxwWpFW2dnZ4efnR8WKFbl8+TI//vgjO3fuxMjISOloIo3TaDQMHDiQP//8E4AOHTowe/ZsDA0NP9g2S5Ys1K9fn/r166PVarlx4wa7d+9m165dHDt2jICAAGbMmMGMGTMwMTGhRo0aeHp64uXlRd68eZMtc0BAgG52y9GjR4mLi9PdZ2VlhYeHB/Xq1cPDw4OsWbMm23GF+K/cuXNz+PDhBAWYQ4cOSQHmC5w+fZpJkyaxadMmNBoNAN999x39+/enSZMmGBi8e5s3dOhQBg8ezIEDB5g3bx5bt27lzJkznDlzhj59+tC8eXM6depE2bJlk7W4umrVKqZPnw68m4FcsGDBZNu3EEIIIYQQ6UWSiy/Xr19n4sSJHD16lOfPnxMbG/vR7VQqFfHx8UkOKIRIXoGBgbrCS4kSJejYsSM3btzg+vXrXL9+neDgYO7fv8/9+/fZuXNngse6urp+tCiT0m283rx580Fh5f3nfy/6/DGmpqZUrFiRZcuWoa+vn6I5RfqWO3dudu7cSbVq1di/fz8dO3Zk6dKlcqW/SFR0dDRt2rRh/fr1AIwaNYqhQ4d+0ZhRqVS659G+ffsSHh7OgQMHdMWYJ0+esHv3bnbv3k2PHj3Inz+/blbM17b8iouLw9/fnx07drBjxw5u3ryZ4P4CBQpQv3596tWrR+XKlT9aOBIipeTOnZtDhw5RvXp1bt26Rc2aNTl06BBOTk5KR0tzNBoNu3btYtKkSRw9elR3u4eHB/3796dGjRofff7R09PDzc0NNzc3Xrx4wdKlS5k/fz537txh/vz5zJ8/n5IlS9KpUydatmyJtbX1N+W8fPkyHTt2BN4Vf7y9vb9pf0IIIYQQQqRXSSq+nDhxgtq1a+umttvZ2ZElS5ZkDSaESH6hoaF4enoSEBBAnjx58PPz++Dq0uDg4ATFmPcfz58/5/Hjxzx+/Jg9e/YkeIyzs/NHizL29vZflEur1fLixYtECywhISGffLy1tTX58uUjb968H3x2dnaWk+fii5UpU4b169dTv359li9fjqurK2PGjFE6lkiDQkJC8Pb25p9//sHQ0JBFixbRqlWrJO/PwsKChg0b0rBhQ7RaLdeuXdO1Jzt+/Dh37txh+vTpTJ8+HVNTU2rWrKmbFZM7d+6P5vPz82PHjh34+fnx5s0b3X0GBgZUrVqVevXqUbduXQoUKJDk3EIkhzx58uhmwNy8eVM3A0YKMO/ExMSwYsUKJk+ezI0bN4B3v8ctWrSgX79+FC9e/Iv35eDgQP/+/enXrx9Hjx5l/vz5bNiwgUuXLvHbb7/Rv39/fHx86NSpE5UqVfrq11CvX7+mUaNGREVFUadOHf7444+verwQQgghhBAZSZKKL4MHDyYqKopevXoxbNgwbG1tkzuXECKZRUdH4+3tzcWLF3F0dGTv3r0fbethb2+Pvb09VatWTXD769evuXHjxgeFmUePHvH8+XOeP3/OgQMHEjwma9asHxRl9PT0EhRW3v87IiLik/mdnJw+WlzJly+fPAeJZOXp6cm8efPo0KEDY8eOxdXVlV9//VXpWCINCQgIwMvLi9u3b2NlZcXmzZupUaNGsu1fpVJRrFgxihUrxoABAwgLC2P//v26YsyzZ8/YuXOnbnZiwYIF8fLyombNmly/fp0dO3bwzz//6FoRwbsLZby8vKhfvz7u7u5YWVklW14hkkOePHl0M2Bu3rypmwHj6OiodDTFvH79mjlz5vDXX3/pWsVaWlrSuXNnevToQfbs2ZO8b5VKRbVq1ahWrRp//fUXy5cvZ/78+Vy7do2lS5eydOlSihQpQseOHWnTpg12dnaf3adGo6FVq1bcu3ePXLlysWrVKpl1LIQQQgghMrUkFV/Onj1LqVKlmDJlSnLnEUKkgPj4eJo1a8aRI0ewtLTEz8/vq9cPsLGxoVKlSh8sWB8eHs7Nmzc/mClz//59Xr58ydGjRxO0xkiMSqUiR44cHxRW8uXLR548eWR2nUhVP//8M48fP8bX15du3brh6OhIo0aNlI4l0oBTp05Rv359goODyZEjB7t27aJo0aIpekxLS0saN25M48aN0Wq1XL58WdeezN/fn1u3bnHr1i2mTp2a4HHFixenXr161KtXj/Lly8tJUJHm5c2bV1eAuXHjBjVr1uTgwYOZrgDz8OFDpk6dyoIFC3j79i0A2bJlo1evXnTq1CnZi6e2trb07NmTHj16cPLkSebPn8/atWu5fv06ffr0YdCgQfz444906tSJ6tWrJzobZuTIkezatQsTExM2bdokF8cIIYQQQohML0nFFyMjI/Lly5fcWYQQKUCr1dK5c2e2bt2KsbEx27Zto1SpUsm2fwsLC8qVK0e5cuUS3B4ZGcmtW7c+KMoAH529kitXLoyNjZMtlxDfavjw4Tx+/JiFCxfStGlTZsyYQZcuXZSOJRS0ZcsWWrRoQVRUFN999x07duxI9YXBVSoVJUuWpGTJkgwaNIg3b96wb98+XXuyvHnz6tqJ5cqVK1WzCZEc8uXLp2tBdv36dd0MGAcHB6WjpbgLFy4wadIk1q1bh1qtBt4VUfv3789PP/2EkZFRih5fpVJRsWJFKlasyNSpU1m1ahXz58/nwoULrF69mtWrV5MvXz46duxIu3btEhTFduzYoWsxNm/ePEqXLp2iWYUQQgghhEgPklR8+eGHH7hy5UpyZxFCpIDBgwezaNEi9PT0WLt2LdWqVUuV45qZmVG6dGl58y3SLZVKxezZs1Gr1SxZsoSuXbsSEBDAxIkT0dPTUzqeSGXTp0+nd+/eaLVavLy8WLt2bZqYkWdtbU3Tpk1p2rSp0lGESDb58uXTzYB5X4A5ePBghizAaLVa9uzZw59//pmgfWutWrXo378/7u7uiqxdZ2VlRZcuXejSpQvnzp1j/vz5rFy5krt37zJo0CCGDRuGt7c3nTp1Infu3Lo1r7p160br1q1TPa8QQgghhBBpUZLOHo0dO5bHjx8zefLk5M4jhEhGkydPZsKECQAsWLAAb29vhRMJkb68X0h99OjRwLvfqSZNmhAZGalwMpFa1Go1vXv3plevXglmEqaFwosQGVn+/Pk5fPgwLi4uXLt2jVq1ahEcHKx0rGQTGxvLsmXLKFmyJJ6enhw4cAB9fX1atGjBuXPn2L9/P3Xq1FGk8PJfZcqUYc6cOTx//pwFCxZQvnx54uPj2bhxIx4eHhQpUoTQ0FAqVaokbamFEEIIIYT4lyTNfDl//jzt27dnwIABbN++HTc3N7Jnz57om4M2bdp8U0ghxNdbunQp/fr1A2DChAm0b99e4URCpE8qlYqhQ4eSJ08e2rVrx+bNm6levTrbtm3DyclJ6XhpnlarJTIykpcvXxIcHMzLly91H2FhYbi5uVGxYkWlY35UVFQUrVq1YtOmTcC759L+/funiZOhQmQG+fPn182AuXr1qm4GjL29vdLRkiw0NJTFixczffp0nj59CoC5uTmdOnWiV69e5MyZU+GEicuSJQsdOnSgQ4cOXLlyhfnz57N8+XLevHmDk5MT69evT/HWaEIIIYQQQqQnSSq+tGvXDpVKhVar5ejRoxw7duyj22m1WlQqlRRfhEhl27dvp0OHDgD069ePAQMGKJxIiPSvefPmuLq60rBhQ86cOUOFChXYuXNnii+2ntbExcUREhKSoIjy76LKfwsswcHBREdHJ7q/ESNGULNmTYYNG/bJhZxTW3BwMA0aNODkyZMYGRmxdOlSmjVrpnQsITKdAgUK6NaAuXr1KrVq1eLAgQPprgDz5MkTlixZQuvWrQkPDwfAycmJnj170rlzZ2xsbBRO+HWKFy/OX3/9xYQJE9i3bx8lSpTAxcVF6VhCCCGEEEKkKUkqvgwfPjzNnBwRQiR07NgxfHx8UKvVtGvXjokTJyodSYgM44cffuDEiRPUrVuXO3fuULlyZTZs2EDt2rWVjvZN4uLiOHPmDKdPnyY4OJjXr18nWlR58+ZNko5hbGyMvb09WbNm1X2o1Wo2b97MwYMHOXjwIBUrVmTYsGF4enoq+jrj9u3beHl5ERAQgI2NDVu2bKFq1aqK5REisytQoACHDh2iRo0aXLlyhVq1anHw4EGyZs2qdLTPevToEb6+vixfvpz4+HgAChcuTL9+/WjZsiXGxsYKJ/w2pqamNGjQQOkYQgghhBBCpElJKr74+vomcwwhRHK4dOkS9evXJzo6mvr16zN//nwplAqRzPLnz8+JEydo2LAhx48fx9PTk7lz5/Lzzz8rHS1JDh8+TJcuXbh58+YXP0alUmFra5ugmPK5f5ubm3/0+ejhw4dMmjSJBQsW6ApbpUuXZujQoTRq1Ag9vSQtT5dk/v7+NGjQgJCQEHLnzs2uXbsoVKhQqmYQQnyoYMGCuhZk7wswBw4cSLMFmJCQEMaOHcvff/9NTEwMAEWLFmX06NE0aNAg1Z/bhBBCCCGEEKkvScUXIUTac+/ePTw8PAgNDaVKlSqsXbsWAwP5FRciJdjZ2bF//35+/vlnVq1aRYcOHbh79y6jR49ONyfUXrx4Qb9+/Vi+fDkAVlZW2NvbkzdvXhwcHD5aSHn/tY2NDfr6+smSI2fOnMycOZOhQ4cyZcoUZs+ezYULF2jSpAlFihRhyJAh/PTTT6nyfLZ+/Xpat25NTEwM5cqVY/v27Tg6Oqb4cYUQX+Z9AaZGjRpcvnyZ2rVrc+DAAezs7JSOpvP27VumTZvGxIkTCQsLA6B69eqMHDmSV69e4eXllW7+TgghhBBCCCG+zTefyXj+/Dn+/v48e/YMlUqFs7MzlSpVwtnZOTnyCSG+QGBgIO7u7gQGBlKiRAm2bduGqamp0rGEyNCMjY1ZsWIFefPmZdSoUYwbN4579+6xZMkSTExMlI6XKI1Gw/z58xk0aBBv3rxBpVLx66+/4uvry4kTJ/Dy8sLQ0DDVczk7OzNp0iQGDhzI9OnTmTFjBtevX6dVq1YMHz6cwYMH06ZNmxRZzFmr1TJlyhT69esHQIMGDVi1ahXm5ubJfiwhxLcpVKiQbgbMpUuXqF27Nvv371e8ABMXF8eCBQsYOXIkgYGBAJQsWZLx48dTp04d4uPj2bVrl6IZhRBCCCGEEKkryZddBQcH06xZM3LkyIGPjw+9evWiZ8+e+Pj4kCNHDpo1a0ZwcHByZhVCfERoaCgeHh4EBASQJ08e/Pz8sLa2VjqWEJmCSqVi5MiRLFmyBENDQ9auXUutWrXS7N+/CxcuUKlSJX799VfevHlD6dKlOXnyJLNmzUoziz1nzZqVUaNG8fDhQ8aMGYOdnR337t2jU6dO5MuXj5kzZxIVFZVsx1Or1fTo0UNXePntt9/YtGmTFF6ESMPeF2AcHR25ePEibm5uvHr1SpEsGo2GtWvXUqRIEbp27UpgYCC5c+dm5cqVnD9/Hg8PD2kBK4QQQgghRCaVpOJLaGgoVatWZd26dRgZGdGgQQN69OhBjx498Pb2xtjYmHXr1lG1alVCQ0OTO7MQ4v9FRUXRoEEDLl26hKOjI3v37pVZZ0IooG3btuzZswdra2v8/f2pWLEit27dUjqWTnh4OL1796Zs2bKcOnUKCwsLpk+fzunTp/n++++VjvdRVlZWDBkyhIcPHzJlyhScnZ15/Pgx3bt3J3fu3EyaNInw8PBvOsbbt29p3LgxM2fOBGDy5Mn89ddfydZSTQiRcgoXLszBgwdxcHDgwoUL1K5dO9ULMPv37+f777+nWbNm3L17F3t7e/766y9u3rxJixYtpL2YEEIIIYQQmVyS3hGMHz+eW7du0bRpUx48eMDmzZuZOnUqU6dOZdOmTTx48AAfHx9u3brFhAkTkjuzEAKIj4+nefPmHD16FEtLS/z8/MibN6/SsYTItGrUqIG/vz+5c+cmICCAihUrcuTIEUUzabVaNmzYQKFChZg2bRoajQYfHx9u3rxJjx490sW6UObm5vTu3Zt79+4xe/ZscubMSVBQEAMGDCBXrlyMHDmS169ff/V+g4KCqF69Otu2bcPY2Jj169fTp08fuUJdiHSkSJEiHDp0SFeASa0ZMOfOncPNzQ03NzfOnTtHlixZ8PX1JSAggO7du6dIe0QhhBBCCCFE+pOk4svmzZtxdXVlxYoV2Nvbf3B/1qxZWb58Oa6urmzcuPGbQwohEtJqtXTu3JmtW7dibGzMtm3bKFWqlNKxhMj0ChcuzMmTJ6lQoQKvX7/Gzc1Nt6B9agsICMDLy4umTZvy7Nkz8ubNi5+fH2vXrsXFxUWRTN/CxMSEX3/9lTt37rB48WIKFCjAq1evGDFiBDlz5mTIkCFf3O7t5s2bVKhQgbNnz2JnZ8fBgwdp0qRJCn8HQoiUUKRIEQ4ePIi9vT3nz5/Hzc0tSQXZL3Hnzh1++uknypYty/79+zE0NKRHjx4EBAQwYsQILCwsUuS4QgghhBBCiPQpScWXhw8fUrly5U8uyGtoaEjlypV59OhRksMJIT5u0KBBLFq0CD09PdauXUu1atWUjiSE+H8ODg66k/lxcXG0adMGX19ftFptqhw/JiaG0aNHU6xYMfz8/DAyMmL48OFcuXKFOnXqpEqGlGRoaEi7du24fv06a9asoXjx4oSHhzNu3Dhy5sxJ7969efr0aaKPP3r0KJUqVeLBgwfkzZuXEydOUKlSpVT8DoQQya1o0aIcOnQoxQowz58/p0uXLhQpUoR169ahUqlo1aoVt27dYvr06Tg4OCTbsYQQQgghhBAZR5KKL6amprx8+fKz2718+RJTU9OkHEIIkYg///yTiRMnArBgwQK8vb0VTiSE+C9TU1PWrl3LwIEDAfjjjz9o27YtMTExKXrcgwcPUrJkSX7//Xeio6OpVasWV65c4Y8//shwf4/19fX56aefuHjxIlu3bqVcuXJERUUxbdo08uTJw6+//sr9+/cTPGb16tW6k7IVKlTgxIkT5M+fX6HvQAiRnIoWLaqbAXPu3Dnc3d158+bNN+0zNDSUYcOGkS9fPubMmUN8fDxeXl5cvHiR5cuXkzt37uQJL4QQQgghhMiQklR8KVOmDEeOHOHcuXOJbnPu3DkOHz5M2bJlkxxOCJHQ0qVL6d+/PwATJkygffv2CicSQiRGT0+P8ePHM2/ePPT19Vm+fDl16tRJkfUIgoKCaNWqFbVq1eLWrVs4OjqycuVK9u3bR4ECBZL9eGmJnp4eDRo04NSpU+zZs4cqVaoQGxvL3LlzyZ8/P23btuXmzZtMmDCBFi1aEBsbS+PGjXUnaYUQGUexYsU4ePAgWbNm5ezZs7i5uSWpABMdHc2UKVPImzcvY8aMITIykgoVKnDkyBF27txJiRIlkj+8EEIIIYQQIsNJUvGld+/exMXFUatWLf744w/u3LlDbGwssbGx3LlzB19fX2rXro1araZ3797JnVmITGn79u106NABgH79+jFgwACFEwkhvkSnTp3YtWsXFhYWHDlyhIoVKxIQEJAs+1ar1cyePZuCBQuycuVKVCoV3bp14+bNm7Ro0SJTLR6vUqlwd3fn6NGjHDlyBHd3d9RqNcuWLaNw4cIMGjQIePcaZt26dRluJpAQ4p33BRg7OzvOnj37VTNg1Go1S5YsoWDBgvTt25eQkBAKFy7M5s2b8ff3p2rVqikbXgghhBBCCJGhJKn44uXlxZgxY4iIiGDkyJEUKlQIMzMzzMzMKFSoEKNGjSI8PJzRo0fj6emZ3JmFyHSOHTuGj48ParWadu3a6dqOCSHSB3d3d/755x9cXV25ffs2FSpUwN/f/5v2ef78eSpWrEjXrl0JDQ3lu+++49SpU8ycORNra+vkCZ5OVa1alT179nD69Glda0aVSsX06dOZMmUK+vr6CicUQqSk4sWL6wowZ86coU6dOoSGhia6vVarZdu2bZQsWZL27dvz6NEjsmXLxsKFC7l8+TINGzbMVMVsIYQQQgghRPJIUvEFYPDgwZw8eZJWrVqRK1cuDA0NMTQ0JFeuXLRu3ZoTJ04wePDg5MwqRIp68+YNx44d4++//6Zz585UqlQJS0tLcufOTY8ePThw4ABxcXGpnuvSpUvUr1+f6Oho6tevz/z58+UEgBDpUPHixTl16hRlypTh5cuX1KxZk7Vr1371fsLCwujZsyflypXjzJkzWFpa8tdff3H69GnKlSuXAsnTr3LlyrFlyxZu3LjBxYsX6dGjh9KRhBCppESJEhw4cAA7OztOnz6daAHmn3/+oUqVKnh7e3Pt2jVsbGyYOHEid+7c4eeff8bAwECB9EIIIYQQQoiM4JveTZQtW5alS5cmVxYhUkVcXBy3b9/m8uXLXL58mStXrnD58mUeP3780e3Dw8OZMWMGM2bMwMrKCi8vL7y9vfH09MTS0jJFs967dw8PDw9CQ0P54YcfWLt2rZwEECIdc3Z25siRI7Ro0YJt27bRrFkz7t27x6BBgz5bVNVqtaxfv55evXrx/PlzAJo1a8aUKVNwdnZOjfjpVqFChZSOIIRQQMmSJTlw4AA1a9bk1KlTeHh4sGfPHiwtLbl69SpDhgxh+/btAJiamtKzZ08GDhyY6WcPCiGEEEIIIZKHnMUVGZZWqyUwMDBBgeXy5cvcuHGD2NjYjz4mR44cFC9enBIlSlCiRAmKFi3K/fv32bp1K9u3byc4OJjVq1ezevVqDA0NqVGjBg0aNKBBgwa4uroma/7AwEDc3NwIDAykRIkSbN++XdYoECIDMDc3Z9OmTfTr149p06YxZMgQAgICmD17NoaGhh99zN27d+nWrRt79+4FIF++fMyaNQs3N7fUjC6EEOnO+wJMrVq1OHnyJHXq1KFgwYIsW7YMrVaLvr4+HTp0YMSIEbi4uCgdVwghhBBCCJGBSPFFZAiRkZFcv35dV2B5X3B5+fLlR7fPkiVLgiJL8eLFKV68+EevdCxevDgNGjRArVZz8uRJtm3bxtatW7l16xZ79+5l7969/Pbbb3z33Xc0aNAAb29vSpYs+U2twUJDQ/Hw8ODevXvkyZMHPz8/uQpTiAxEX1+fqVOnkjdvXnr27MnChQt58OABGzZsSPC7HhMTw4QJExg7diwxMTEYGRkxePBgBg0ahImJiXLfgBBCpCOlSpVi//79ugLMyZMnAWjSpAmjR4+mYMGCCicUQgghhBBCZERfVHzJkydPkg+gUqkICAhI8uOF+DeNRkNgYCBbt27l+vXruhktd+7cQavVfrC9np4e+fPnT1BkKVGiBDlz5kRP7+uWPNLX16dy5cpUrlyZCRMmcOvWLV0hxt/fn/Pnz3P+/Hl8fX3JkSOHrhBTrVq1RK9m/5ioqCgaNGjApUuXcHR0ZO/evdJSSIgM6rfffiNXrlw0a9aMAwcOULlyZXbu3EmuXLnYv38/Xbt25c6dOwDUrl2bv//+mwIFCiicWggh0p/SpUtz4MABfHx8yJ07N2PGjJF1soQQQgghhBAp6ouKLw8ePEClUn305PbnyMLgIjkcP36cYcOGce7cOSIiIj66TdasWSlZsmSCIkuRIkVSrFVXwYIF6d+/P/379+fFixfs3LmTrVu3snfvXh49esTMmTOZOXOmbp2YBg0a4OnpiZWVVaL7jI+Pp1mzZhw9ehRLS0v8/PzImzdviuQXQqQN9erV49ixY9SrV4/r169ToUIFfvjhBzZu3AiAk5MTU6dO5aeffpK/qUII8Q1Kly6tK2gLIYQQQgghREr7qrZjZcqUoVWrVnh7e6fo2hOzZ89m9uzZPHjwAICiRYsyfPhwPD09gXdrefzxxx/MmzeP169fU758ef7++2+KFi2q20dMTAz9+vVj9erVREVFUatWLWbNmkX27NlTLLdIfrGxsYwYMYIJEyboin8GBgYUK1ZMN5vlfbHF0dFRsROTDg4OtG/fnvbt2xMZGcmBAwd068S8ePEiwTox1atXx9vbm/r165MjRw7dPrRaLb/88gvbtm3D2NiYbdu2UapUKUW+HyFE6ipdujSnTp2ibt26XL58mY0bN6JSqejWrRujR4/+ZNFWCCGEEEIIIYQQQqQ9X1R8WbNmDStXrsTPz48+ffowfPhwfvzxR1q2bEnNmjWT/YR39uzZGT9+PPny5QNg6dKleHt7c+HCBYoWLcrEiROZMmUKS5YsoUCBAowePRo3Nzdu3bqFhYUFAL169WL79u2sWbMGOzs7+vbtS7169Th37hz6+vrJmlekjKtXr9KqVSsuXboEQLt27ejZsycBAQE0aNDgq1p5pSYzMzPq169P/fr1UavVnD59mq1bt7J161Zu3rzJvn372LdvH7/99hulS5fG29ubBg0asGbNGhYvXoyenh5r166lWrVqSn8rQohUlD17do4fP86vv/5KUFAQ48ePp2zZskrHEkIIIYQQQgghhBBJ8EXFFx8fH3x8fHj16hVr1qxhxYoVLFmyhKVLl+Lk5ESLFi1o2bJlsl2lX79+/QRfjxkzhtmzZ3Py5EmKFCnCtGnTGDp0KI0bNwbeFWccHR1ZtWoVnTt3JjQ0lIULF7J8+XJq164NwIoVK3B1dWX//v3UqVMnWXKKlKHRaJg2bRqDBw8mNjaWrFmzMm/ePBo1akRcXBwPHz5UOuIX09fXp2LFilSsWJHx48dz+/btBOvEXLhwgQsXLuDr66t7zPz58/H29lYutBBCMRYWFqxcuVLpGEIIIYQQQgghhBDiG31V2zFbW1u6du1K165defDgAcuXL2fVqlVMnjyZKVOmULhwYVq3bk2LFi1wdXVNloBqtZr169fz9u1bKlasyP379wkMDMTd3V23jbGxMdWqVcPf35/OnTtz7tw54uLiEmzj4uJCsWLF8Pf3T7T4EhMTQ0xMjO7rsLAwAOLi4oiLi0uW70d82sOHD+nYsSNHjhwBwMvLizlz5uDk5JTg/yG9/n/kzp2bnj170rNnT4KDg9m9ezfbtm1j//79REZGMm7cOFq3bp1uvz/xP+l9rIrMR8asSC9krIr0SMatSC9krIr0SMatSE9kvIr0Rsbsx33pz0Olfb+Qxjc4c+YMK1euZO3atbx48QJ7e3sCAwO/aZ9XrlyhYsWKREdHkyVLFlatWoWXlxf+/v5UrlyZp0+f4uLiotv+l19+4eHDh+zZs4dVq1bRvn37BIUUAHd3d3Lnzs3cuXM/ekxfX1/++OOPD25ftWoVZmZm3/T9iE/TarUcPnyY+fPnExkZiYmJCe3bt8fd3T1TLDAdExPDmzdvcHR0VDqKEEIIIYQQQgghhBBCiERERkbSokULQkNDsbS0THS7r5r5kpicOXOSJ08eXFxcCAo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",
       "text/plain": [
        "<Figure size 2000x700 with 2 Axes>"
       ]
@@ -382,22 +382,24 @@
     "plot_df = plot_df[plot_df['unique_id']=='Airline1'].drop(['unique_id','trend','y_[lag12]'], axis=1).iloc[-50:]\n",
     "\n",
     "ax1.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
-    "ax1.plot(plot_df['ds'], plot_df['NHITS1'], c='blue', label='median')\n",
+    "ax1.plot(plot_df['ds'], plot_df['NHITS'], c='blue', label='median')\n",
     "ax1.fill_between(x=plot_df['ds'][-12:], \n",
-    "                 y1=plot_df['NHITS1-lo-90'][-12:].values,\n",
-    "                 y2=plot_df['NHITS1-hi-90'][-12:].values,\n",
+    "                 y1=plot_df['NHITS-lo-90'][-12:].values,\n",
+    "                 y2=plot_df['NHITS-hi-90'][-12:].values,\n",
     "                 alpha=0.4, label='level 90')\n",
-    "ax1.set_title('AirPassengers Forecast', fontsize=18)\n",
+    "ax1.set_title('AirPassengers Forecast - Uncertainty quantification using Conformal Prediction', fontsize=18)\n",
     "ax1.set_ylabel('Monthly Passengers', fontsize=15)\n",
+    "ax1.set_xticklabels([])\n",
     "ax1.legend(prop={'size': 10})\n",
     "ax1.grid()\n",
     "\n",
     "ax2.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
-    "ax2.plot(plot_df['ds'], plot_df['NHITS'], c='blue', label='median')\n",
+    "ax2.plot(plot_df['ds'], plot_df['NHITS1'], c='blue', label='median')\n",
     "ax2.fill_between(x=plot_df['ds'][-12:], \n",
-    "                 y1=plot_df['NHITS-lo-90'][-12:].values,\n",
-    "                 y2=plot_df['NHITS-hi-90'][-12:].values,\n",
+    "                 y1=plot_df['NHITS1-lo-90'][-12:].values,\n",
+    "                 y2=plot_df['NHITS1-hi-90'][-12:].values,\n",
     "                 alpha=0.4, label='level 90')\n",
+    "ax2.set_title('AirPassengers Forecast - Uncertainty quantification using Normal distribution', fontsize=18)\n",
     "ax2.set_ylabel('Monthly Passengers', fontsize=15)\n",
     "ax2.set_xlabel('Timestamp [t]', fontsize=15)\n",
     "ax2.legend(prop={'size': 10})\n",

From 241d76a1fb45218a80b7f529c730591c3a7454c5 Mon Sep 17 00:00:00 2001
From: t-minus <gohjingqiang@gmail.com>
Date: Tue, 8 Oct 2024 15:47:56 +0000
Subject: [PATCH 24/25] CrossValidation can provide conformal-intervals outputs
 if refit=True

---
 nbs/core.ipynb         | 40 +++++++++++++++++++++++++++++++++++++++-
 neuralforecast/core.py |  8 ++++++++
 2 files changed, 47 insertions(+), 1 deletion(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index a0d508c83..37d9e9b32 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -1173,6 +1173,8 @@
     "        id_col: str = 'unique_id',\n",
     "        time_col: str = 'ds',\n",
     "        target_col: str = 'y',\n",
+    "        prediction_intervals: Optional[PredictionIntervals] = None,\n",
+    "        level: Optional[List[Union[int, float]]] = None,\n",
     "        **data_kwargs\n",
     "    ) -> DataFrame:\n",
     "        \"\"\"Temporal Cross-Validation with core.NeuralForecast.\n",
@@ -1211,6 +1213,10 @@
     "            Column that identifies each timestep, its values can be timestamps or integers.\n",
     "        target_col : str (default='y')\n",
     "            Column that contains the target.            \n",
+    "        level : list of ints or floats, optional (default=None)\n",
+    "            Confidence levels between 0 and 100.            \n",
+    "        prediction_intervals : PredictionIntervals, optional (default=None)\n",
+    "            Configuration to calibrate prediction intervals (Conformal Prediction).            \n",
     "        data_kwargs : kwargs\n",
     "            Extra arguments to be passed to the dataset within each model.\n",
     "\n",
@@ -1281,7 +1287,8 @@
     "                    verbose=verbose,\n",
     "                    id_col=id_col,\n",
     "                    time_col=time_col,\n",
-    "                    target_col=target_col,                                     \n",
+    "                    target_col=target_col,\n",
+    "                    prediction_intervals=prediction_intervals,                                     \n",
     "                )\n",
     "                predict_df: Optional[DataFrame] = None\n",
     "            else:\n",
@@ -1297,6 +1304,7 @@
     "                futr_df=futr_df,\n",
     "                sort_df=sort_df,\n",
     "                verbose=verbose,\n",
+    "                level=level,\n",
     "                **data_kwargs\n",
     "            )\n",
     "            preds = ufp.join(preds, cutoffs, on=id_col, how='left')\n",
@@ -3391,6 +3399,36 @@
     "nf.fit(AirPassengersPanel_train, prediction_intervals=prediction_intervals)\n",
     "preds = nf.predict(futr_df=AirPassengersPanel_test, level=[90])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a2bd299",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# test cross validation can support conformal prediction\n",
+    "prediction_intervals = PredictionIntervals()\n",
+    "\n",
+    "# refit=False, no conformal predictions outputs\n",
+    "nf = NeuralForecast(models=[NHITS(h=12, input_size=24, max_steps=1)], freq='M')\n",
+    "cv1 = nf.cross_validation(\n",
+    "    AirPassengersPanel_train, \n",
+    "    prediction_intervals=prediction_intervals,\n",
+    "    level=[30]\n",
+    "    )\n",
+    "assert not any([col in cv1.columns for col in ['NHITS-lo-30', 'NHITS-hi-30']])\n",
+    "\n",
+    "# refit=True, we have conformal predictions outputs\n",
+    "cv2 = nf.cross_validation(\n",
+    "    AirPassengersPanel_train, \n",
+    "    prediction_intervals=prediction_intervals,\n",
+    "    refit=True,\n",
+    "    level=[30, 70]\n",
+    ")\n",
+    "assert all([col in cv2.columns for col in ['NHITS-lo-30', 'NHITS-hi-30']])"
+   ]
   }
  ],
  "metadata": {
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index 180a8a004..50b2652cb 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -1137,6 +1137,8 @@ def cross_validation(
         id_col: str = "unique_id",
         time_col: str = "ds",
         target_col: str = "y",
+        prediction_intervals: Optional[PredictionIntervals] = None,
+        level: Optional[List[Union[int, float]]] = None,
         **data_kwargs,
     ) -> DataFrame:
         """Temporal Cross-Validation with core.NeuralForecast.
@@ -1175,6 +1177,10 @@ def cross_validation(
             Column that identifies each timestep, its values can be timestamps or integers.
         target_col : str (default='y')
             Column that contains the target.
+        level : list of ints or floats, optional (default=None)
+            Confidence levels between 0 and 100.
+        prediction_intervals : PredictionIntervals, optional (default=None)
+            Configuration to calibrate prediction intervals (Conformal Prediction).
         data_kwargs : kwargs
             Extra arguments to be passed to the dataset within each model.
 
@@ -1246,6 +1252,7 @@ def cross_validation(
                     id_col=id_col,
                     time_col=time_col,
                     target_col=target_col,
+                    prediction_intervals=prediction_intervals,
                 )
                 predict_df: Optional[DataFrame] = None
             else:
@@ -1261,6 +1268,7 @@ def cross_validation(
                 futr_df=futr_df,
                 sort_df=sort_df,
                 verbose=verbose,
+                level=level,
                 **data_kwargs,
             )
             preds = ufp.join(preds, cutoffs, on=id_col, how="left")

From bf62b3cc3dc3942dc028be215e1e34352b65c2f4 Mon Sep 17 00:00:00 2001
From: Olivier Sprangers <osprangers@gmail.com>
Date: Thu, 10 Oct 2024 17:24:31 +0200
Subject: [PATCH 25/25] add_protections

---
 nbs/core.ipynb         | 105 +++++++++++++++++++++++++----------------
 neuralforecast/core.py |  63 ++++++++++++++++++-------
 2 files changed, 110 insertions(+), 58 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 37d9e9b32..c9c12bb4b 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -571,6 +571,16 @@
     "                target_col=target_col,\n",
     "            )\n",
     "            self.sort_df = sort_df\n",
+    "            if prediction_intervals is not None:\n",
+    "                self.prediction_intervals = prediction_intervals\n",
+    "                self._cs_df = self._conformity_scores(\n",
+    "                    df=df,\n",
+    "                    id_col=id_col,\n",
+    "                    time_col=time_col,\n",
+    "                    target_col=target_col,\n",
+    "                    static_df=static_df,\n",
+    "                )\n",
+    "\n",
     "        elif isinstance(df, SparkDataFrame):\n",
     "            if static_df is not None and not isinstance(static_df, SparkDataFrame):\n",
     "                raise ValueError(\n",
@@ -585,6 +595,10 @@
     "                target_col=target_col,\n",
     "                distributed_config=distributed_config,\n",
     "            )\n",
+    "\n",
+    "            if prediction_intervals is not None:\n",
+    "                raise NotImplementedError(\"Prediction intervals are not supported for distributed training.\")\n",
+    "\n",
     "        elif isinstance(df, Sequence):\n",
     "            if not all(isinstance(val, str) for val in df):\n",
     "                raise ValueError(\"All entries in the list of files must be of type string\")        \n",
@@ -598,6 +612,10 @@
     "            )\n",
     "            self.uids = self.dataset.indices\n",
     "            self.last_dates = self.dataset.last_times\n",
+    "            \n",
+    "            if prediction_intervals is not None:\n",
+    "                raise NotImplementedError(\"Prediction intervals are not supported for local files.\")\n",
+    "            \n",
     "        elif df is None:\n",
     "            if verbose:\n",
     "                print(\"Using stored dataset.\")\n",
@@ -610,16 +628,6 @@
     "            if self.dataset.min_size < val_size:\n",
     "                warnings.warn('Validation set size is larger than the shorter time-series.')\n",
     "\n",
-    "        if prediction_intervals is not None:\n",
-    "            self.prediction_intervals = prediction_intervals\n",
-    "            self._cs_df = self._conformity_scores(\n",
-    "                df=df,\n",
-    "                id_col=id_col,\n",
-    "                time_col=time_col,\n",
-    "                target_col=target_col,\n",
-    "                static_df=static_df,\n",
-    "            )\n",
-    "\n",
     "        # Recover initial model if use_init_models\n",
     "        if use_init_models:\n",
     "            self._reset_models()\n",
@@ -1213,10 +1221,10 @@
     "            Column that identifies each timestep, its values can be timestamps or integers.\n",
     "        target_col : str (default='y')\n",
     "            Column that contains the target.            \n",
-    "        level : list of ints or floats, optional (default=None)\n",
-    "            Confidence levels between 0 and 100.            \n",
     "        prediction_intervals : PredictionIntervals, optional (default=None)\n",
     "            Configuration to calibrate prediction intervals (Conformal Prediction).            \n",
+    "        level : list of ints or floats, optional (default=None)\n",
+    "            Confidence levels between 0 and 100. Use with prediction_intervals.            \n",
     "        data_kwargs : kwargs\n",
     "            Extra arguments to be passed to the dataset within each model.\n",
     "\n",
@@ -1236,7 +1244,8 @@
     "                raise Exception('`test_size - h` should be module `step_size`')\n",
     "            n_windows = int((test_size - h) / step_size) + 1\n",
     "        else:\n",
-    "            raise Exception('you must define `n_windows` or `test_size` but not both')       \n",
+    "            raise Exception('you must define `n_windows` or `test_size` but not both')    \n",
+    "\n",
     "        # Recover initial model if use_init_models.\n",
     "        if use_init_models:\n",
     "            self._reset_models()\n",
@@ -1246,7 +1255,19 @@
     "                FutureWarning,\n",
     "            )\n",
     "            df = df.reset_index(id_col)            \n",
+    "\n",
+    "        # Checks for prediction intervals\n",
+    "        if prediction_intervals is not None or level is not None:\n",
+    "            if level is None:\n",
+    "                warnings.warn('Level not provided, using level=[90].')\n",
+    "                level = [90]\n",
+    "            if prediction_intervals is None:\n",
+    "                raise Exception('You must set prediction_intervals to use level.')\n",
+    "            if not refit:\n",
+    "                raise Exception('Passing prediction_intervals and/or level is only supported with refit=True.')    \n",
+    "\n",
     "        if not refit:\n",
+    "\n",
     "            return self._no_refit_cross_validation(\n",
     "                df=df,\n",
     "                static_df=static_df,\n",
@@ -1644,7 +1665,7 @@
     "        id_col: str, \n",
     "        time_col: str,\n",
     "        target_col: str,\n",
-    "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
+    "        static_df: Optional[DataFrame],\n",
     "    ) -> DataFrame:\n",
     "        \"\"\"Compute conformity scores.\n",
     "        \n",
@@ -1653,11 +1674,11 @@
     "        \n",
     "        The exception is raised by the PredictionIntervals data class.\n",
     "\n",
-    "        df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,\n",
-    "        id_col: str = 'unique_id',\n",
-    "        time_col: str = 'ds',\n",
-    "        target_col: str = 'y',\n",
-    "        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,\n",
+    "        df: DataFrame,\n",
+    "        id_col: str,\n",
+    "        time_col: str,\n",
+    "        target_col: str,\n",
+    "        static_df: Optional[DataFrame],\n",
     "        \"\"\"\n",
     "        if self.prediction_intervals is None:\n",
     "            raise AttributeError('Please rerun the `fit` method passing a valid prediction_interval setting to compute conformity scores')\n",
@@ -1689,7 +1710,7 @@
     "            abs_err = abs(cv_results[model] - cv_results[target_col])\n",
     "            cv_results = ufp.assign_columns(cv_results, model, abs_err)\n",
     "        dropped = list(set(cv_results.columns) - set(kept))\n",
-    "        return ufp.drop_columns(cv_results, dropped)    "
+    "        return ufp.drop_columns(cv_results, dropped)           "
    ]
   },
   {
@@ -1916,14 +1937,6 @@
     "test_eq(init_fcst, after_fcst)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d94486f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2921,6 +2934,23 @@
     "from polars.testing import assert_frame_equal"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad51c803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "#| polars\n",
+    "renamer = {'unique_id': 'uid', 'ds': 'time', 'y': 'target'}\n",
+    "inverse_renamer = {v: k for k, v in renamer.items()}\n",
+    "AirPassengers_pl = polars.from_pandas(AirPassengersPanel_train)\n",
+    "AirPassengers_pl = AirPassengers_pl.rename(renamer)\n",
+    "AirPassengersStatic_pl = polars.from_pandas(AirPassengersStatic)\n",
+    "AirPassengersStatic_pl = AirPassengersStatic_pl.rename({'unique_id': 'uid'})"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2931,18 +2961,15 @@
     "#| hide\n",
     "#| polars\n",
     "models = [LSTM(h=12, input_size=24, max_steps=5, hist_exog_list=['zeros'], scaler_type='robust')]\n",
+    "\n",
+    "# Pandas\n",
     "nf = NeuralForecast(models=models, freq='M')\n",
     "nf.fit(AirPassengersPanel_train, static_df=AirPassengersStatic)\n",
     "insample_preds = nf.predict_insample()\n",
     "preds = nf.predict()\n",
     "cv_res = nf.cross_validation(df=AirPassengersPanel_train, static_df=AirPassengersStatic)\n",
     "\n",
-    "renamer = {'unique_id': 'uid', 'ds': 'time', 'y': 'target'}\n",
-    "inverse_renamer = {v: k for k, v in renamer.items()}\n",
-    "AirPassengers_pl = polars.from_pandas(AirPassengersPanel_train)\n",
-    "AirPassengers_pl = AirPassengers_pl.rename(renamer)\n",
-    "AirPassengersStatic_pl = polars.from_pandas(AirPassengersStatic)\n",
-    "AirPassengersStatic_pl = AirPassengersStatic_pl.rename({'unique_id': 'uid'})\n",
+    "# Polars\n",
     "nf = NeuralForecast(models=models, freq='1mo')\n",
     "nf.fit(\n",
     "    AirPassengers_pl,\n",
@@ -3413,12 +3440,10 @@
     "\n",
     "# refit=False, no conformal predictions outputs\n",
     "nf = NeuralForecast(models=[NHITS(h=12, input_size=24, max_steps=1)], freq='M')\n",
-    "cv1 = nf.cross_validation(\n",
-    "    AirPassengersPanel_train, \n",
-    "    prediction_intervals=prediction_intervals,\n",
-    "    level=[30]\n",
-    "    )\n",
-    "assert not any([col in cv1.columns for col in ['NHITS-lo-30', 'NHITS-hi-30']])\n",
+    "test_fail(\n",
+    "    nf.cross_validation, \n",
+    "    \"Passing prediction_intervals and/or level is only supported with refit=True.\",\n",
+    "    args=(AirPassengersPanel_train, prediction_intervals, [30, 70]))\n",
     "\n",
     "# refit=True, we have conformal predictions outputs\n",
     "cv2 = nf.cross_validation(\n",
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index 50b2652cb..85214f57a 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -503,6 +503,16 @@ def fit(
                 target_col=target_col,
             )
             self.sort_df = sort_df
+            if prediction_intervals is not None:
+                self.prediction_intervals = prediction_intervals
+                self._cs_df = self._conformity_scores(
+                    df=df,
+                    id_col=id_col,
+                    time_col=time_col,
+                    target_col=target_col,
+                    static_df=static_df,
+                )
+
         elif isinstance(df, SparkDataFrame):
             if static_df is not None and not isinstance(static_df, SparkDataFrame):
                 raise ValueError(
@@ -517,6 +527,12 @@ def fit(
                 target_col=target_col,
                 distributed_config=distributed_config,
             )
+
+            if prediction_intervals is not None:
+                raise NotImplementedError(
+                    "Prediction intervals are not supported for distributed training."
+                )
+
         elif isinstance(df, Sequence):
             if not all(isinstance(val, str) for val in df):
                 raise ValueError(
@@ -532,6 +548,12 @@ def fit(
             )
             self.uids = self.dataset.indices
             self.last_dates = self.dataset.last_times
+
+            if prediction_intervals is not None:
+                raise NotImplementedError(
+                    "Prediction intervals are not supported for local files."
+                )
+
         elif df is None:
             if verbose:
                 print("Using stored dataset.")
@@ -546,16 +568,6 @@ def fit(
                     "Validation set size is larger than the shorter time-series."
                 )
 
-        if prediction_intervals is not None:
-            self.prediction_intervals = prediction_intervals
-            self._cs_df = self._conformity_scores(
-                df=df,
-                id_col=id_col,
-                time_col=time_col,
-                target_col=target_col,
-                static_df=static_df,
-            )
-
         # Recover initial model if use_init_models
         if use_init_models:
             self._reset_models()
@@ -1177,10 +1189,10 @@ def cross_validation(
             Column that identifies each timestep, its values can be timestamps or integers.
         target_col : str (default='y')
             Column that contains the target.
-        level : list of ints or floats, optional (default=None)
-            Confidence levels between 0 and 100.
         prediction_intervals : PredictionIntervals, optional (default=None)
             Configuration to calibrate prediction intervals (Conformal Prediction).
+        level : list of ints or floats, optional (default=None)
+            Confidence levels between 0 and 100. Use with prediction_intervals.
         data_kwargs : kwargs
             Extra arguments to be passed to the dataset within each model.
 
@@ -1201,6 +1213,7 @@ def cross_validation(
             n_windows = int((test_size - h) / step_size) + 1
         else:
             raise Exception("you must define `n_windows` or `test_size` but not both")
+
         # Recover initial model if use_init_models.
         if use_init_models:
             self._reset_models()
@@ -1210,7 +1223,21 @@ def cross_validation(
                 FutureWarning,
             )
             df = df.reset_index(id_col)
+
+        # Checks for prediction intervals
+        if prediction_intervals is not None or level is not None:
+            if level is None:
+                warnings.warn("Level not provided, using level=[90].")
+                level = [90]
+            if prediction_intervals is None:
+                raise Exception("You must set prediction_intervals to use level.")
+            if not refit:
+                raise Exception(
+                    "Passing prediction_intervals and/or level is only supported with refit=True."
+                )
+
         if not refit:
+
             return self._no_refit_cross_validation(
                 df=df,
                 static_df=static_df,
@@ -1635,7 +1662,7 @@ def _conformity_scores(
         id_col: str,
         time_col: str,
         target_col: str,
-        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
+        static_df: Optional[DataFrame],
     ) -> DataFrame:
         """Compute conformity scores.
 
@@ -1644,11 +1671,11 @@ def _conformity_scores(
 
         The exception is raised by the PredictionIntervals data class.
 
-        df: Optional[Union[DataFrame, SparkDataFrame, Sequence[str]]] = None,
-        id_col: str = 'unique_id',
-        time_col: str = 'ds',
-        target_col: str = 'y',
-        static_df: Optional[Union[DataFrame, SparkDataFrame]] = None,
+        df: DataFrame,
+        id_col: str,
+        time_col: str,
+        target_col: str,
+        static_df: Optional[DataFrame],
         """
         if self.prediction_intervals is None:
             raise AttributeError(