def METHOD_NAME(self, text_preprocess, _, target=0):
    """Calculates the captum insights
    Args:
        text_preprocess (tensor): Tensor of the Text Input
        _ (str): The Raw text data specified in the input request
        target (int): Defaults to 0, the user needs to specify the target
                      for the captum explanation.
    Returns:
        (dict): Returns a dictionary of the word token importances
    """
    text_tensor, all_tokens = text_preprocess
    token_reference = TokenReferenceBase()
    logger.info("input_text shape %s", len(text_tensor.shape))
    logger.info("get_insights target %s", target)
    offsets = torch.tensor([0]).to(self.device)
    all_tokens = self.get_word_token(all_tokens)
    logger.info("text_tensor tokenized shape %s", text_tensor.shape)
    reference_indices = token_reference.generate_reference(
        text_tensor.shape[0], device=self.device
    ).squeeze(0)
    logger.info("reference indices shape %s", reference_indices.shape)
    # all_tokens = self.get_word_token(text)
    attributions = self.lig.attribute(
        text_tensor,
        reference_indices,
        additional_forward_args=(offsets),
        return_convergence_delta=False,
        target=target,
    )
    logger.info("attributions shape %s", attributions.shape)
    attributions_sum = self.summarize_attributions(attributions)
    response = {}
    response["importances"] = attributions_sum.tolist()
    response["words"] = all_tokens
    return [response]