style: format code with Autopep8, Black, ClangFormat, dotnet-format, …

…Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf

This commit fixes the style issues introduced in 918e5b0 according to the output
from Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java
Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt,
Scalafmt, StandardJS, StandardRB, swift-format and Yapf.

Details: None

models/ai_models/neural_networks.py

@@ -1,9 +1,11 @@
 # Import necessary libraries
 import tensorflow as tf
-from tensorflow import keras
 from sklearn.preprocessing import StandardScaler
+from tensorflow import keras
 
 # Define a class for neural networks
+
+
 class NeuralNetwork:
     def __init__(self, input_shape, output_shape, hidden_layers):
         self.input_shape = input_shape
@@ -16,14 +18,22 @@ def _create_model(self):
         model = keras.Sequential()
         model.add(keras.layers.InputLayer(input_shape=self.input_shape))
         for layer in self.hidden_layers:
-            model.add(keras.layers.Dense(layer, activation='relu'))
-        model.add(keras.layers.Dense(self.output_shape, activation='softmax'))
-        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+            model.add(keras.layers.Dense(layer, activation="relu"))
+        model.add(keras.layers.Dense(self.output_shape, activation="softmax"))
+        model.compile(
+            optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
+        )
         return model
 
     def train(self, X_train, y_train, X_test, y_test):
         # Train the neural network model
-        self.model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test))
+        self.model.fit(
+            X_train,
+            y_train,
+            epochs=10,
+            batch_size=128,
+            validation_data=(X_test, y_test),
+        )
 
     def predict(self, X):
         # Make predictions using the trained model
@@ -34,7 +44,10 @@ def evaluate(self, X_test, y_test):
         loss, accuracy = self.model.evaluate(X_test, y_test)
         return accuracy
 
+
 # Define a function to load a neural network model
+
+
 def load_neural_network(model_path):
     # Load the neural network model from a file
     model = tf.keras.models.load_model(model_path)