code: def test_wide_model(pd_df, tmp):
      data, users, items = pd_df
  
      # Test wide model
      # Test if wide column has two original features and one crossed feature
      wide_columns, _ = build_feature_columns(
          users, items, model_type="wide", crossed_feat_dim=10
      )
      assert len(wide_columns) == 3
      # Check crossed feature dimension
      assert wide_columns[2].hash_bucket_size == 10
      # Check model type
      model = build_model(
          os.path.join(tmp, "wide_" + MODEL_DIR),
          wide_columns=wide_columns,
      )
      assert isinstance(model, tf.compat.v1.estimator.LinearRegressor)
      # Test if model train works
      model.train(
          input_fn=pandas_input_fn(
              df=data,
              y_col=DEFAULT_RATING_COL,
              batch_size=1,
              num_epochs=None,
              shuffle=True,
          ),
          steps=1,
      )
  
      # 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 2 for calling a global function with example usage: test_wide_model(pd_df, tmp) without return types