import pandas as pd

def calculate_bollinger_bands(data, column='close', period=21, std_multiplier=1.7):
    """
    Calculates Bollinger Bands for a specified column in the data DataFrame.

    Args:
        data (pd.DataFrame): The input DataFrame containing the price data.
        column (str): The name of the column to calculate the Bollinger Bands on. Default is 'close'.
        period (int): The period over which to calculate the SMA and standard deviation. Default is 21.
        std_multiplier (float): The multiplier for the standard deviation to define the width of the bands. Default is 1.7.

    Returns:
        pd.DataFrame: The input DataFrame augmented with Bollinger Bands ('BB_Middle', 'BB_Upper', 'BB_Lower').
    """
    # Calculate the SMA for the specified period
    data['BB_Middle'] = data[column].rolling(window=period).mean()
    
    # Calculate the standard deviation
    std_dev = data[column].rolling(window=period).std()
    
    # Calculate the upper and lower Bollinger Bands
    data['BB_Upper'] = data['BB_Middle'] + (std_multiplier * std_dev)
    data['BB_Lower'] = data['BB_Middle'] - (std_multiplier * std_dev)
    
    return data

# Example usage:
# Assuming `data` is a pandas DataFrame with a 'close' column:
# data_with_bbands = calculate_bollinger_bands(data, column='close', period=21, std_multiplier=1.7)