code: def test_deep_model(pd_df, tmp):
      data, users, items = pd_df
  
      # Test if deep columns have user and item features
      _, deep_columns = build_feature_columns(
          users, items, model_type="deep"
      )
      assert len(deep_columns) == 2
      # Check model type
      model = build_model(
          os.path.join(tmp, "deep_" + MODEL_DIR),
          deep_columns=deep_columns,
      )
      assert isinstance(model, tf.compat.v1.estimator.DNNRegressor)
      # Test if model train works
      model.train(
          input_fn=pandas_input_fn(
              df=data,
              y_col=DEFAULT_RATING_COL,
              batch_size=1,
              num_epochs=1,
              shuffle=False,
          )
      )
  
      # Close the event file so that the model folder can be cleaned up.
      summary_writer = tf.compat.v1.summary.FileWriterCache.get(
          model.model_dir
      )
      summary_writer.close()
  
file_path: recommenders/tests/unit/recommenders/models/test_wide_deep_utils.py
imports: import os
  import pytest
  import pandas
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL
label_desc: Use image node_id 3 for calling a global function with example usage: test_deep_model(pd_df, tmp) without return types