code: def test_high_cardinality_eigen():
      df = h2o.create_frame(
          rows=10000,
          cols=10,
          categorical_fraction=0.6,
          integer_fraction=0,
          binary_fraction=0,
          real_range=100,
          integer_range=100,
          missing_fraction=0,
          factors=10,
          seed=1234,
      )
      autoencoder = H2OAutoEncoderEstimator(
          categorical_encoding="eigen",
          reproducible=True,
          hidden=[50, 30],
          epochs=5,
          seed=42,
      )
      autoencoder.train(training_frame=df)
  
      mojo = pyunit_utils.download_mojo(autoencoder)
      autoencoder_mojo = h2o.import_mojo(mojo["mojo_zip_path"])
  
      preds_ae_h2o = autoencoder.predict(df)
      preds_ae_mojo = autoencoder_mojo.predict(df)
      assert_frame_equal(
          preds_ae_mojo.as_data_frame(), preds_ae_h2o.as_data_frame()
      )
  
imports: import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
  from pandas.testing import assert_frame_equal
label_desc: Use image node_id 1 for calling a global function with example usage: test_high_cardinality_eigen() without return types