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 ee7b2f7 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

features/synthetic_data_generation/data_generator.py

@@ -1,32 +1,37 @@
 # data_generator.py
 import synthpop
+import tensorflow as tf
 from synthpop import Synthesizer
+from tensorflow.keras.models import Sequential
+
 
 def synthetic_data_generation():
     # Initialize the synthetic data generator
     synthesizer = Synthesizer()
 
     # Define the synthetic data generation algorithm
-    algorithm = synthesizer.add_algorithm('synthetic_data_generation')
+    algorithm = synthesizer.add_algorithm("synthetic_data_generation")
 
     # Run the synthetic data generation algorithm
     data = algorithm.generate(1000)
 
     return data
 
+
 # ai_trainer.py
-import tensorflow as tf
-from tensorflow.keras.models import Sequential
+
 
 def ai_trainer(data):
     # Define the AI training algorithm
     model = Sequential()
-    model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)))
-    model.add(tf.keras.layers.Dense(64, activation='relu'))
-    model.add(tf.keras.layers.Dense(10, activation='softmax'))
+    model.add(tf.keras.layers.Dense(64, activation="relu", input_shape=(10,)))
+    model.add(tf.keras.layers.Dense(64, activation="relu"))
+    model.add(tf.keras.layers.Dense(10, activation="softmax"))
 
     # Compile the AI training algorithm
-    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    model.compile(
+        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
+    )
 
     # Train the AI model
     model.fit(data, epochs=10)