code: def test_explainer_text_hf(
      explainer_class,
      model_type,
      cache_encoder_embeddings,
      tmpdir,
      ray_cluster_2cpu,
  ):
      input_features = [
          text_feature(
              encoder={
                  "type": "auto_transformer",
                  "pretrained_model_name_or_path": "hf-internal-testing/tiny-bert-for-token-classification",
              },
              preprocessing={
                  "cache_encoder_embeddings": cache_encoder_embeddings
              },
          )
      ]
      run_test_explainer_api(
          explainer_class,
          model_type,
          [binary_feature()],
          {},
          tmpdir,
          input_features=input_features,
      )
  
imports: import logging
  import os
  import numpy
  import pandas
  import pytest
  from ludwig.api import LudwigModel
  from ludwig.constants import BATCH_SIZE, BINARY, CATEGORY, MINIMUM_BATCH_SIZE, MODEL_ECD, MODEL_GBM, TYPE
  from ludwig.explain.captum import IntegratedGradientsExplainer
  from ludwig.explain.explainer import Explainer
  from ludwig.explain.explanation import Explanation
  from ludwig.explain.gbm import GBMExplainer
  from tests.integration_tests.utils import binary_feature, category_feature, date_feature, generate_data, image_feature, LocalTestBackend, number_feature, sequence_feature, set_feature, text_feature, timeseries_feature, vector_feature
label_desc: Use image node_id 6 for calling a global function with example usage: test_explainer_text_hf(explainer_class, model_type, cache_encoder_embeddings, tmpdir, ray_cluster_2cpu) without return types