code: def test_hf_ludwig_model_auto_transformers(
      tmpdir, csv_filename, pretrained_model_name_or_path
  ):
      """Tests different AutoModel types to ensure our wrapper handles them correctly.
  
      This is needed because different PretrainedModel implemetnations have different input / output signatures.
      """
      input_features = [
          text_feature(
              preprocessing={
                  "max_sequence_length": 10,
              },
              encoder={
                  "vocab_size": 30,
                  "min_len": 1,
                  "type": "auto_transformer",
                  "pretrained_model_name_or_path": pretrained_model_name_or_path,
              },
          )
      ]
      output_features = [category_feature(decoder={"vocab_size": 2})]
      rel_path = generate_data(
          input_features, output_features, csv_filename
      )
  
      config = {
          "input_features": input_features,
          "output_features": output_features,
          TRAINER: {"train_steps": 1},
      }
      model = LudwigModel(config=config, backend=LocalTestBackend())
  
      # Validates that the defaults associated with the encoder are compatible with Ludwig training.
      with mock.patch(
          "ludwig.encoders.text_encoders.load_pretrained_hf_model_with_hub_fallback",
          side_effect=_load_pretrained_hf_model_no_weights,
      ):
          model.train(dataset=rel_path, output_directory=tmpdir)
  
imports: import json
  import os
  from typing import Optional, Type, Union
  from unittest import mock
  import pytest
  import torch
  import ludwig.schema.encoders.utils
  from ludwig.api import LudwigModel
  from ludwig.constants import ENCODER, ENCODER_OUTPUT, MODEL_ECD, NAME, TEXT, TRAINER
  from ludwig.encoders import text_encoders
  from ludwig.error import ConfigValidationError
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils.data_utils import load_json
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
  from tests.integration_tests.utils import category_feature, clear_huggingface_cache, generate_data, HF_ENCODERS, LocalTestBackend, text_feature
label_desc: Use image node_id 5 for calling a global function with example usage: test_hf_ludwig_model_auto_transformers(tmpdir, csv_filename, pretrained_model_name_or_path) without return types