class_docstr: Transformers handler class for sequence, token classification and question answering.
docstr: In this initialize function, the BERT model is loaded and
  the Layer Integrated Gradients Algorithm for Captum Explanations
  is initialized here.
  Args:
      ctx (context): It is a JSON Object containing information
      pertaining to the model artifacts parameters.
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code: def initialize(self, ctx):
      """In this initialize function, the BERT model is loaded and
      the Layer Integrated Gradients Algorithm for Captum Explanations
      is initialized here.
      Args:
          ctx (context): It is a JSON Object containing information
          pertaining to the model artifacts parameters.
      """
      self.manifest = ctx.manifest
      properties = ctx.system_properties
      model_dir = properties.get("model_dir")
      serialized_file = self.manifest["model"]["serializedFile"]
      model_pt_path = os.path.join(model_dir, serialized_file)
  
      self.device = torch.device(
          "cuda:" + str(properties.get("gpu_id"))
          if torch.cuda.is_available()
          and properties.get("gpu_id") is not None
          else "cpu"
      )
      # read configs for the mode, model_name, etc. from setup_config.json
      setup_config_path = os.path.join(model_dir, "setup_config.json")
      if os.path.isfile(setup_config_path):
          with open(setup_config_path) as setup_config_file:
              self.setup_config = json.load(setup_config_file)
      else:
          logger.warning("Missing the setup_config.json file.")
  
      # Loading the shared object of compiled Faster Transformer Library if Faster Transformer is set
      if self.setup_config["FasterTransformer"]:
          faster_transformer_complied_path = os.path.join(
              model_dir, "libpyt_fastertransformer.so"
          )
          torch.classes.load_library(faster_transformer_complied_path)
      # Loading the model and tokenizer from checkpoint and config files based on the user's choice of mode
      # further setup config can be added.
      if self.setup_config["save_mode"] == "torchscript":
          self.model = torch.jit.load(
              model_pt_path, map_location=self.device
          )
      elif self.setup_config["save_mode"] == "pretrained":
          if self.setup_config["mode"] == "sequence_classification":
              self.model = (
                  AutoModelForSequenceClassification.from_pretrained(
                      model_dir
                  )
              )
          elif self.setup_config["mode"] == "question_answering":
              self.model = (
                  AutoModelForQuestionAnswering.from_pretrained(
                      model_dir
                  )
              )
          elif self.setup_config["mode"] == "token_classification":
              self.model = (
                  AutoModelForTokenClassification.from_pretrained(
                      model_dir
                  )
              )
          elif self.setup_config["mode"] == "text_generation":
              self.model = AutoModelForCausalLM.from_pretrained(
                  model_dir
              )
          else:
              logger.warning("Missing the operation mode.")
          # Using the Better Transformer integration to speedup the inference
          if self.setup_config["BetterTransformer"]:
              from optimum.bettertransformer import BetterTransformer
  
              try:
                  self.model = BetterTransformer.transform(self.model)
              except RuntimeError as error:
                  logger.warning(
                      "HuggingFace Optimum is not supporting this model,for the list of supported models, please refer to this doc,https://huggingface.co/docs/optimum/bettertransformer/overview"
                  )
          # HF GPT2 models options can be gpt2, gpt2-medium, gpt2-large, gpt2-xl
          # this basically place different model blocks on different devices,
          # https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/gpt2/modeling_gpt2.py#L962
          if (
              self.setup_config["model_parallel"]
              and "gpt2" in self.setup_config["model_name"]
          ):
              self.model.parallelize()
          else:
              self.model.to(self.device)
  
      else:
          logger.warning("Missing the checkpoint or state_dict.")
  
      if "gpt2" in self.setup_config["model_name"]:
          self.tokenizer = GPT2TokenizerFast.from_pretrained(
              "gpt2", pad_token="<|endoftext|>"
          )
  
      elif any(
          fname
          for fname in os.listdir(model_dir)
          if fname.startswith("vocab.") and os.path.isfile(fname)
      ):
          self.tokenizer = AutoTokenizer.from_pretrained(
              model_dir,
              do_lower_case=self.setup_config["do_lower_case"],
          )
      else:
          self.tokenizer = AutoTokenizer.from_pretrained(
              self.setup_config["model_name"],
              do_lower_case=self.setup_config["do_lower_case"],
          )
  
      self.model.eval()
      logger.info(
          "Transformer model from path %s loaded successfully",
          model_dir,
      )
  
      # Read the mapping file, index to object name
      mapping_file_path = os.path.join(model_dir, "index_to_name.json")
      # Question answering does not need the index_to_name.json file.
      if not (
          self.setup_config["mode"] == "question_answering"
          or self.setup_config["mode"] == "text_generation"
      ):
          if os.path.isfile(mapping_file_path):
              with open(mapping_file_path) as f:
                  self.mapping = json.load(f)
          else:
              logger.warning("Missing the index_to_name.json file.")
      self.initialized = True
  
imports: import ast
  import json
  import logging
  import os
  from abc import ABC
  import torch
  import transformers
  from captum.attr import LayerIntegratedGradients
  from transformers import AutoModelForCausalLM, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoTokenizer, GPT2TokenizerFast
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 2 for calling the TransformersSeqClassifierHandler obj's underlying member method code with example usage: obj.initialize(ctx) without return types