Question: I have implemented this function to fit the model def fit_model(model, X_train_sequence_tensor,Y_train_sequence_tensor, epochs, val_set, time_windows, scaler): X_column_list = [item for item in val_set.columns.to_list() if item not in ['date', 'user', 'rank','rank_group', 'counts', 'target']] X_val_set = val_set[X_column_list].round(2) X_val_set[X_val_set.columns] = scaler.transform(X_val_set[X_val_set.columns] ) X_val_sequence = get_feature_array(X_val_set , X_column_list, time_windows) X_val_sequence_tensor = tf.convert_to_tensor(X_val_sequence, dtype=tf.float32) Y_column_list = ['target'] Y_val_set = val_set[Y_column_list].round(2) Y_val_sequence = get_feature_array(Y_val_set , Y_column_list, time_windows) Y_val_sequence_tensor = tf.convert_to_tensor(Y_val_sequence, dtype=tf.float32) history = model.fit(X_train_sequence_tensor,Y_train_sequence_tensor, epochs, validation_data=(X_val_sequence_tensor, Y_val_sequence_tensor)) return model, history  but when I call it as fitted_model, history = fit_model(model, X_train_sequence_tensor,Y_train_sequence_tensor, epochs=100, val_set=val_set, time_windows=90, scaler=scaler)  it stops after the first epoch. It does not run for all the 100 as required. I tried to call it outside of the function call and it worked. `# Step 3.2 : Fit the model + We pass some validation for # monitoring validation loss and metrics # at the end of each epoch X_val_set = val_set[X_column_list].round(2) #X_val_set.values = scaler.transform(X_val_set.values) X_val_set[X_val_set.columns] = scaler.transform(X_val_set[X_val_set.columns] ) X_val_sequence = get_feature_array(X_val_set , X_column_list, 90) X_val_sequence_tensor = tf.convert_to_tensor(X_val_sequence, dtype=tf.float32) Y_val_set = val_set[Y_column_list].round(2) Y_val_sequence = get_feature_array(Y_val_set , Y_column_list, 90) Y_val_sequence_tensor = tf.convert_to_tensor(Y_val_sequence, dtype=tf.float32) training_history = cnn1d_bilstm_model.fit(X_train_sequence_tensor,Y_train_sequence_tensor, epochs=200, # We pass some validation for # monitoring validation loss and metrics # at the end of each epoch validation_data=(X_val_sequence_tensor, Y_val_sequence_tensor))  What am I doing wrong?

Answer: If epochs is not explicitly passed, Python may use a default value, which could be None or another unintended value. Explicitly passing epochs=epochs ensures that the function uses the value intended by the caller. Here is updated code: def fit_model(model, X_train_sequence_tensor,Y_train_sequence_tensor, epochs, val_set, time_windows, scaler): X_column_list = [item for item in val_set.columns.to_list() if item not in ['date', 'user', 'rank','rank_group', 'counts', 'target']] X_val_set = val_set[X_column_list].round(2) X_val_set[X_val_set.columns] = scaler.transform(X_val_set[X_val_set.columns] ) X_val_sequence = get_feature_array(X_val_set , X_column_list, time_windows) X_val_sequence_tensor = tf.convert_to_tensor(X_val_sequence, dtype=tf.float32) Y_column_list = ['target'] Y_val_set = val_set[Y_column_list].round(2) Y_val_sequence = get_feature_array(Y_val_set , Y_column_list, time_windows) Y_val_sequence_tensor = tf.convert_to_tensor(Y_val_sequence, dtype=tf.float32) try: history = model.fit(X_train_sequence_tensor, Y_train_sequence_tensor, epochs=epochs, validation_data=(X_val_sequence_tensor, Y_val_sequence_tensor)) except Exception as e: print(f"Training stopped due to an error: {e}") return model, history fitted_model, history = fit_model(model, X_train_sequence_tensor,Y_train_sequence_tensor, epochs=100, val_set=val_set, time_windows=90, scaler=scaler) # Print Training History print("Training Completed Successfully!")