def check_ans_with_model(pred, gt, model, item, dataset_name='MVBench'):
    """
    Checks if the predicted answer matches the ground truth using a given model.

    Args:
        pred (str): The predicted answer.
        gt (str): The ground truth answer.
        model: A machine learning model used for additional verification.
        item (dict): An item containing information about the question or task.
        dataset_name (str, optional): Name of the dataset being used. Defaults to 'MVBench'.

    Returns:
        bool: True if the predicted answer matches the ground truth, False otherwise.
    """
    # Initialize flag to track match status
    flag = False

    # Preprocess prediction and ground truth by converting to lowercase and splitting into options and contents
    pred_list = pred.lower().strip().split(' ')
    pred_option, _ = pred_list[0], ' '.join(pred_list[1:])
    gt_list = gt.lower().strip().split(' ')
    gt_option, gt_content = gt_list[0], ' '.join(gt_list[1:])

    # Remove trailing period from ground truth content if presen
    if gt_content[-1] == '.':
        gt_content = gt_content[:-1]

    # Check for matching conditions
    # Condition 1: If the predicted option is a substring of the ground truth option
    if pred_option.replace('.', '') in gt_option:
        flag = True
    # Condition 2: If the ground truth option is a substring of the predicted option
    elif gt_option in pred_option:
        flag = True
    # Condition 3: Use the provided model to verify the answer
    elif extract_answer_from_item(model, item, dataset_name)['opt'] == item['answer']:
        flag = True

    return flag

    Checks if the predicted answer matches the ground truth using a given model.

    Args:
        pred (str): The predicted answer.
        gt (str): The ground truth answer.
        model: A machine learning model used for additional verification.
        item (dict): An item containing information about the question or task.
        dataset_name (str, optional): Name of the dataset being used. Defaults to 'MVBench'.

    Returns:
        bool: True if the predicted answer matches the ground truth, False otherwise.
    
vlmeval/dataset/utils/tamperbench.py