code: def test_hf_ludwig_model_e2e(tmpdir, csv_filename, encoder_name):
      """Tests HuggingFace encoders end-to-end.
  
      This test validates the following:
          1. Encoder config defaults are compatible with Ludwig experiments.
          2. Ludwig correctly updates the encoder config with the parameters introduced by the HF encoder.
          3. Ludwig correctly loads checkpoints containing HF encoder weights.
      """
      input_features = [
          text_feature(
              encoder={
                  "vocab_size": 30,
                  "min_len": 1,
                  "type": encoder_name,
              }
          )
      ]
      output_features = [category_feature(decoder={"vocab_size": 2})]
      rel_path = generate_data(
          input_features, output_features, csv_filename
      )
  
      if encoder_name == "auto_transformer":
          # need to explciitly set the pretrained model name for auto_transformer
          input_features[0][ENCODER][
              "pretrained_model_name_or_path"
          ] = "hf-internal-testing/tiny-bert-for-token-classification"
  
      config = {
          "input_features": input_features,
          "output_features": output_features,
          TRAINER: {"train_steps": 1},
      }
      model = LudwigModel(config=config, backend=LocalTestBackend())
  
      with mock.patch(
          "ludwig.encoders.text_encoders.load_pretrained_hf_model_with_hub_fallback",
          side_effect=_load_pretrained_hf_model_no_weights,
      ):
          # Validates that the defaults associated with the encoder are compatible with Ludwig training.
          _, _, _, results_dir = model.experiment(
              dataset=rel_path, output_directory=tmpdir
          )
  
          # Validate that the saved config reflects the parameters introduced by the HF encoder.
          # This ensures that the config updates after initializing the encoder.
          mismatched_config_params = get_mismatched_config_params(
              results_dir, model
          )
          if len(mismatched_config_params) > 0:
              raise AssertionError(
                  f"Config parameters mismatched with encoder parameters: "
                  f"{json.dumps(mismatched_config_params, indent=4)}"
              )
  
          # Validate the model can be loaded.
          # This ensures that the config reflects the internal architecture of the encoder.
          LudwigModel.load(os.path.join(results_dir, "model"))
      clear_huggingface_cache()
  
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 3 for calling a global function with example usage: test_hf_ludwig_model_e2e(tmpdir, csv_filename, encoder_name) without return types