docstr: This function initialize and calls the layer integrated gradient to get word importance
  of the input text if captum explanation has been selected through setup_config
  Args:
      input_batch (int): Batches of tokens IDs of text
      text (str): The Text specified in the input request
      target (int): The Target can be set to any acceptable label under the user's discretion.
  Returns:
      (list): Returns a list of importances and words.
code: def get_insights(self, input_batch, text, target):
      """This function initialize and calls the layer integrated gradient to get word importance
      of the input text if captum explanation has been selected through setup_config
      Args:
          input_batch (int): Batches of tokens IDs of text
          text (str): The Text specified in the input request
          target (int): The Target can be set to any acceptable label under the user's discretion.
      Returns:
          (list): Returns a list of importances and words.
      """
      data = json.loads(text)
      text = data["text"]
      target = data["target"]
  
      if self.setup_config["captum_explanation"]:
          embedding_layer = getattr(
              self.model, self.setup_config["embedding_name"]
          )
          embeddings = embedding_layer.embeddings
          self.lig = LayerIntegratedGradients(
              captum_sequence_forward, embeddings
          )
      else:
          logger.warning(
              "Captum Explanation is not chosen and will not be available"
          )
  
      self.target = target
  
      input_ids, ref_input_ids, attention_mask = construct_input_ref(
          text, self.tokenizer, self.device, self.setup_config["mode"]
      )
      all_tokens = get_word_token(input_ids, self.tokenizer)
      response = {}
      response["words"] = all_tokens
  
      attributions, delta = self.lig.attribute(
          inputs=input_ids,
          baselines=ref_input_ids,
          target=self.target,
          additional_forward_args=(attention_mask, 0, self.model),
          return_convergence_delta=True,
      )
  
      attributions_sum = summarize_attributions(attributions)
      response["importances"] = attributions_sum.tolist()
      response["delta"] = delta[0].tolist()
  
      return [response]
  
file_path: serve/kubernetes/kserve/kf_request_json/v2/bert/Transformer_kserve_handler.py
imports: import torch
  import logging
  from Transformer_handler_generalized import TransformersSeqClassifierHandler, captum_sequence_forward, construct_input_ref, summarize_attributions, get_word_token
  import json
  from captum.attr import LayerIntegratedGradients
label_desc: Use image node_id 3 for calling the TransformersKserveHandler obj's underlying member method code with example usage: obj.get_insights(input_batch, text, target) and returns: list