code: def test_text_adapter_lora(tmpdir, backend, ray_cluster_2cpu):
      input_features = [
          text_feature(
              encoder={
                  "type": "auto_transformer",
                  "pretrained_model_name_or_path": "hf-internal-testing/tiny-bert-for-token-classification",
                  "trainable": True,
                  "adapter": {"type": "lora"},
              },
          ),
      ]
      output_features = [binary_feature()]
  
      data_csv_path = os.path.join(tmpdir, "dataset.csv")
      dataset = generate_data(
          input_features, output_features, data_csv_path
      )
  
      config = {
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          COMBINER: {TYPE: "concat", "output_size": 14},
          TRAINER: {EPOCHS: 1},
      }
      model = run_test_suite(config, dataset, backend)
  
      state_dict = model.model.state_dict()
  
      # check that at least one of the keys contains the word "lora_" denoting a lora parameter
      assert any("lora_" in key for key in state_dict.keys())
  
imports: import os
  import pytest
  from ludwig.constants import COMBINER, EPOCHS, INPUT_FEATURES, OUTPUT_FEATURES, TRAINER, TYPE
  from tests.integration_tests.utils import binary_feature, generate_data, run_test_suite, text_feature
label_desc: Use image node_id 1 for calling a global function with example usage: test_text_adapter_lora(tmpdir, backend, ray_cluster_2cpu) without return types