code: def test_input_feature_transformer_encoder(feature_gen):
      """Tests that a transformer hyperparameter divisibility error is correctly recognized for a specific feature.
  
      Transformers require that `hidden_size % num_heads == 0`. 9 and 18 were selected as test values because they were
      the values from the original error.
      """
      config = {
          INPUT_FEATURES: [
              number_feature(),
              feature_gen(
                  **{
                      ENCODER: {
                          TYPE: "transformer",
                          "hidden_size": 9,
                          "num_heads": 18,
                      }
                  }
              ),
          ],
          OUTPUT_FEATURES: [binary_feature()],
      }
  
      with pytest.raises(ConfigValidationError):
          ModelConfig.from_dict(config)
  
      config[INPUT_FEATURES][1][ENCODER]["hidden_size"] = 18
      config[INPUT_FEATURES][1][ENCODER]["num_heads"] = 9
  
      ModelConfig.from_dict(config)
  
imports: import pytest
  from ludwig.constants import DEFAULTS, ENCODER, INPUT_FEATURES, NAME, OUTPUT_FEATURES, SEQUENCE, TEXT, TIMESERIES, TYPE
  from ludwig.error import ConfigValidationError
  from ludwig.schema.model_config import ModelConfig
  from tests.integration_tests.utils import binary_feature, number_feature, sequence_feature, text_feature, timeseries_feature
label_desc: Use image node_id 2 for calling a global function with example usage: test_input_feature_transformer_encoder(feature_gen) without return types