def METHOD_NAME(self, model_wraper, text, target=1):
    """Captum explanations handler
    Args:
        data_preprocess (Torch Tensor):
        Preprocessed data to be used for captum
        raw_data (list): The unprocessed data to get target from the request
    Returns:
        dict : A dictionary response with the explanations response.
    """
    vis_data_records_base = []
    model_wrapper = AGNewsmodelWrapper(self.model)
    tokenizer = BertTokenizer(self.VOCAB_FILE)
    model_wrapper.eval()
    model_wrapper.zero_grad()
    encoding = tokenizer.encode_plus(
        self.text, return_attention_mask=True, return_tensors="pt", add_special_tokens=False
    )
    input_ids = encoding["input_ids"]
    attention_mask = encoding["attention_mask"]
    input_ids = input_ids.to(self.device)
    attention_mask = attention_mask.to(self.device)
    input_embedding_test = model_wrapper.model.bert_model.embeddings(input_ids)
    preds = model_wrapper(input_embedding_test, attention_mask)
    out = np.argmax(preds.cpu().detach(), axis=1)
    out = out.item()
    ig_1 = IntegratedGradients(model_wrapper)
    attributions, delta = ig_1.attribute(  # pylint: disable=no-member
        input_embedding_test,
        n_steps=500,
        return_convergence_delta=True,
        target=1,
    )
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0].cpu().numpy().tolist())
    feature_imp_dict = {}
    feature_imp_dict["words"] = tokens
    attributions_sum = self.summarize_attributions(attributions)
    feature_imp_dict["importances"] = attributions_sum.tolist()
    feature_imp_dict["delta"] = delta[0].tolist()
    self.add_attributions_to_visualizer(
        attributions, tokens, self.score_func(preds), out, 2, 1, delta, vis_data_records_base
    )
    return [feature_imp_dict]