what does this code do ?
def load_pretrained_models(stock_symbol: str, progress_callback=None) -> List[Tuple]:
    """
    Loads pre-trained models for a given stock symbol.

    Parameters:
        stock_symbol (str): The ticker symbol of the stock.
        progress_callback (callable): Function to send progress updates.

    Returns:
        List[Tuple]: List of tuples containing models and their names.
    """
    models = []
    try:
        # Load LSTM Model
        lstm_path = os.path.join(TRAINED_MODELS_DIR, f'{stock_symbol}_lstm_model.h5')
        if os.path.exists(lstm_path):
            import tensorflow as tf
            lstm_model = tf.keras.models.load_model(lstm_path)
            models.append((lstm_model, 'lstm'))
            if progress_callback:
                progress_callback(f"LSTM model loaded for {stock_symbol}.")
        else:
            if progress_callback:
                progress_callback(f"LSTM model not found for {stock_symbol}.")

        # Load XGBoost Model
        xgb_path = os.path.join(TRAINED_MODELS_DIR, f'{stock_symbol}_xgboost_model.json')
        if os.path.exists(xgb_path):
            xgb_model = XGBRegressor()
            xgb_model.load_model(xgb_path)
            models.append((xgb_model, 'xgboost'))
            if progress_callback:
                progress_callback(f"XGBoost model loaded for {stock_symbol}.")
        else:
            if progress_callback:
                progress_callback(f"XGBoost model not found for {stock_symbol}.")

        # Load Random Forest Model
        rf_path = os.path.join(TRAINED_MODELS_DIR, f'{stock_symbol}_random_forest_model.pkl')
        if os.path.exists(rf_path):
            rf_model = pd.read_pickle(rf_path)
            models.append((rf_model, 'random_forest'))
            if progress_callback:
                progress_callback(f"Random Forest model loaded for {stock_symbol}.")
        else:
            if progress_callback:
                progress_callback(f"Random Forest model not found for {stock_symbol}.")

        # Load Linear Regression Model
        lr_path = os.path.join(TRAINED_MODELS_DIR, f'{stock_symbol}_linear_regression_model.pkl')
        if os.path.exists(lr_path):
            lr_model = pd.read_pickle(lr_path)
            models.append((lr_model, 'linear_regression'))
            if progress_callback:
                progress_callback(f"Linear Regression model loaded for {stock_symbol}.")
        else:
            if progress_callback:
                progress_callback(f"Linear Regression model not found for {stock_symbol}.")
    except Exception as e:
        if progress_callback:
            progress_callback(f"Error loading models for {stock_symbol}: {e}")

    return models

def evaluate_model(model, X_test, y_test, model_name: str, stock_symbol: str, progress_callback=None) -> Optional[np.ndarray]:
    """
    Evaluates a given model and returns predictions.

    Parameters:
        model: Trained model.
        X_test (array-like): Test features.
        y_test (array-like): Test target.
        model_name (str): Name of the model.
        stock_symbol (str): Stock symbol.
        progress_callback (callable): Function to send progress updates.

    Returns:
        np.ndarray: Predictions.
    """
    try:
        if model_name == 'lstm':
            y_pred = model.predict(X_test).flatten()
        else:
            y_pred = model.predict(X_test)
        mse = mean_squared_error(y_test, y_pred)
        if progress_callback:
            progress_callback(f"{model_name.upper()} Evaluation for {stock_symbol} - MSE: {mse}")
        return y_pred
    except Exception as e:
        if progress_callback:
            progress_callback(f"Error evaluating {model_name} for {stock_symbol}: {e}")
        return None

def weighted_average_prediction(models: List[Tuple], X_test, y_test, stock_symbol: str, progress_callback=None) -> Optional[np.ndarray]:
    """
    Calculates a weighted average of predictions from multiple models based on inverse MSE.

    Parameters:
        models (List[Tuple]): List of tuples containing models and their names.
        X_test (array-like): Test features.
        y_test (array-like): Test target.
        stock_symbol (str): Stock symbol.
        progress_callback (callable): Function to send progress updates.

    Returns:
        np.ndarray: Weighted average predictions.
    """
    predictions = []
    weights = []

    for model, model_name in models:
        try:
            if model_name == 'lstm':
                y_pred = model.predict(X_test).flatten()
            else:
                y_pred = model.predict(X_test)
            mse = mean_squared_error(y_test, y_pred)
            weight = 1 / mse if mse != 0 else 0
            predictions.append(y_pred * weight)
            weights.append(weight)
            if progress_callback:
                progress_callback(f"Model: {model_name}, MSE: {mse}, Weight: {weight}")
        except Exception as e:
            if progress_callback:
                progress_callback(f"Error predicting with {model_name}: {e}")

    if not weights:
        if progress_callback:
            progress_callback("No valid model predictions available.")
        return None

    weighted_sum = np.sum(predictions, axis=0)
    total_weight = np.sum(weights)
    final_prediction = weighted_sum / total_weight if total_weight != 0 else None

    return final_prediction

def visualize_predictions(stock_symbol: str, actual: np.ndarray, predicted: np.ndarray, save: bool = False, progress_callback=None):
    """
    Plots actual vs predicted stock prices.

    Parameters:
        stock_symbol (str): The ticker symbol of the stock.
        actual (np.ndarray): Actual closing prices.
        predicted (np.ndarray): Predicted closing prices.
        save (bool): Whether to save the plot as an image.
        progress_callback (callable): Function to send progress updates.
    """
    try:
        plt.figure(figsize=(14, 7))
        plt.plot(actual, color='blue', label='Actual Prices')
        plt.plot(predicted, color='red', linestyle='--', label='Predicted Prices')
        plt.title(f'{stock_symbol} Actual vs Predicted Prices')
        plt.xlabel('Time')
        plt.ylabel('Stock Price')
        plt.legend()
        plt.grid(True)

        if save:
            plot_path = os.path.join(PLOTS_DIR, f'{stock_symbol}_prediction.png')
            plt.savefig(plot_path)
            log_message(f"Saved plot for {stock_symbol} at {plot_path}")
            if progress_callback:
                progress_callback(f"Saved plot for {stock_symbol} at {plot_path}")

        plt.close()
    except Exception as e:
        if progress_callback:
            progress_callback(f"Error visualizing predictions for {stock_symbol}: {e}")

def process_stock(stock_symbol: str, start_date: str, end_date: str, interval: str, train_mode: str, progress_callback=None) -> Optional[float]:
    """
    Processes a single stock: fetches data, preprocesses, trains/loads models, makes predictions, and visualizes results.

    Parameters:
        stock_symbol (str): The ticker symbol of the stock.
        start_date (str): Start date for data fetching.
        end_date (str): End date for data fetching.
        interval (str): Data interval.
        train_mode (str): Mode - 'train' or 'use'.
        progress_callback (callable): Function to send progress updates.

    Returns:
        Optional[float]: Final prediction for the stock, or None if failed.
    """
    progress_callback(f"Processing stock: {stock_symbol}")
    stock_data = fetch_stock_data(stock_symbol, start_date, end_date, interval)

    if stock_data is None or stock_data.empty:
        progress_callback(f"No data found for {stock_symbol}. Skipping...")
        return None

    stock_data, scaler = preprocess_data(stock_data)
    X = stock_data[['SMA', 'EMA', 'RSI', 'MACD', 'Bollinger High', 'Bollinger Low', 'ATR']].values
    y = stock_data['Close'].values

    # Split into training and testing
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

    # Reshape for LSTM
    X_train_lstm = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
    X_test_lstm = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

    models = []

    if train_mode == 'train':
        # Train LSTM Model
        if progress_callback:
            progress_callback("Training LSTM Model...")
        lstm_model, lstm_mse = train_lstm_model(X_train_lstm, y_train, X_test_lstm, y_test, stock_symbol)
        if lstm_model:
            models.append((lstm_model, 'lstm'))

        # Train XGBoost Model
        if progress_callback:
            progress_callback("Training XGBoost Model...")
        xgb_model, xgb_mse = train_xgboost_model(X_train, y_train, X_test, y_test, stock_symbol)
        if xgb_model:
            models.append((xgb_model, 'xgboost'))

        # Train Random Forest Model
        if progress_callback:
            progress_callback("Training Random Forest Model...")
        rf_model, rf_mse = train_random_forest_model(X_train, y_train, X_test, y_test, stock_symbol)
        if rf_model:
            models.append((rf_model, 'random_forest'))

        # Train Linear Regression Model
        if progress_callback:
            progress_callback("Training Linear Regression Model...")
        lr_model, lr_mse = train_linear_regression_model(X_train, y_train, stock_symbol)
        if lr_model:
            models.append((lr_model, 'linear_regression'))

        progress_callback(f"All models trained for {stock_symbol}.")
    else:
        # Load pre-trained models
        if progress_callback:
            progress_callback("Loading pre-trained models...")
        models = load_pretrained_models(stock_symbol, progress_callback)
        if not models:
            progress_callback(f"No pre-trained models available for {stock_symbol}. Skipping prediction.")
            return None
        progress_callback(f"Pre-trained models loaded for {stock_symbol}.")

    # Make Predictions
    if not models:
        progress_callback(f"No models available for {stock_symbol}. Skipping prediction.")
        return None

    # Make predictions and compute weighted average
    progress_callback("Making Predictions...")
    final_prediction = weighted_average_prediction(models, X_test, y_test, stock_symbol, progress_callback)

    if final_prediction is not None:
        final_pred_value = final_prediction[-1]
        progress_callback(f"Final Weighted Prediction for {stock_symbol}: {final_pred_value}")
        # Visualize Predictions
        progress_callback("Visualizing Predictions...")
        visualize_predictions(stock_symbol, y_test, final_prediction, save=True, progress_callback=progress_callback)
        return final_pred_value
    else:
        progress_callback(f"Failed to compute final prediction for {stock_symbol}.")
        return None

def multi_stock_prediction(stock_symbols: List[str], start_date: str, end_date: str, interval: str, train_mode: str, progress_callback=None) -> Dict[str, float]:
    """
    Processes multiple stocks either by training models or using pre-trained models.

    Parameters:
        stock_symbols (List[str]): List of stock ticker symbols.
        start_date (str): Start date for data fetching.
        end_date (str): End date for data fetching.
        interval (str): Data interval.
        train_mode (str): Mode - 'train' or 'use'.
        progress_callback (callable): Function to send progress updates.

    Returns:
        Dict[str, float]: Final predictions per stock.
    """
    predictions = {}
    for stock in stock_symbols:
        pred = process_stock(stock, start_date, end_date, interval, train_mode, progress_callback)
        if pred is not None:
            predictions[stock] = pred
    return predictions