def METHOD_NAME(
    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)