code: def convert_tf_checkpoint_to_pytorch(
      task,
      reset_position_index_per_cell,
      tf_checkpoint_path,
      tapas_config_file,
      pytorch_dump_path,
  ):
      # Initialise PyTorch model.
      # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
      # TapasConfig to False.
  
      # initialize configuration from json file
      config = TapasConfig.from_json_file(tapas_config_file)
      # set absolute/relative position embeddings parameter
      config.reset_position_index_per_cell = (
          reset_position_index_per_cell
      )
  
      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
          model = TapasForQuestionAnswering(config=config)
      elif task == "WTQ":
          # run_task_main.py hparams
          config.num_aggregation_labels = 4
          config.use_answer_as_supervision = True
          # hparam_utils.py hparams
          config.answer_loss_cutoff = 0.664694
          config.cell_selection_preference = 0.207951
          config.huber_loss_delta = 0.121194
          config.init_cell_selection_weights_to_zero = True
          config.select_one_column = True
          config.allow_empty_column_selection = False
          config.temperature = 0.0352513
  
          model = TapasForQuestionAnswering(config=config)
      elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          config.num_aggregation_labels = 4
          config.use_answer_as_supervision = False
          # hparam_utils.py hparams
          config.answer_loss_cutoff = 36.4519
          config.cell_selection_preference = 0.903421
          config.huber_loss_delta = 222.088
          config.init_cell_selection_weights_to_zero = True
          config.select_one_column = True
          config.allow_empty_column_selection = True
          config.temperature = 0.763141
  
          model = TapasForQuestionAnswering(config=config)
      elif task == "TABFACT":
          model = TapasForSequenceClassification(config=config)
      elif task == "MLM":
          model = TapasForMaskedLM(config=config)
      elif task == "INTERMEDIATE_PRETRAINING":
          model = TapasModel(config=config)
      else:
          raise ValueError(f"Task {task} not supported.")
  
      print(f"Building PyTorch model from configuration: {config}")
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
  
      # Save pytorch-model (weights and configuration)
      print(f"Save PyTorch model to {pytorch_dump_path}")
      model.save_pretrained(pytorch_dump_path)
  
      # Save tokenizer files
      print(f"Save tokenizer files to {pytorch_dump_path}")
      tokenizer = TapasTokenizer(
          vocab_file=tf_checkpoint_path[:-10] + "vocab.txt",
          model_max_length=512,
      )
      tokenizer.save_pretrained(pytorch_dump_path)
  
      print(
          "Used relative position embeddings:",
          model.config.reset_position_index_per_cell,
      )
  
imports: import argparse
  from transformers import TapasConfig, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasTokenizer, load_tf_weights_in_tapas
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_tf_checkpoint_to_pytorch(task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path) without return types