code: def shuffling_large():
      print("Reading in Arcene training data for binomial modeling.")
      train_data = h2o.upload_file(
          path=pyunit_utils.locate(
              "smalldata/arcene/shuffle_test_version/arcene.csv"
          )
      )
      train_data_shuffled = h2o.upload_file(
          path=pyunit_utils.locate(
              "smalldata/arcene/shuffle_test_version/arcene_shuffled.csv"
          )
      )
  
      print("Create model on original Arcene dataset.")
      h2o_model = H2OGeneralizedLinearEstimator(
          family="binomial", lambda_search=True, alpha=0.5
      )
      h2o_model.train(
          x=list(range(1000)), y=1000, training_frame=train_data
      )
  
      print("Create second model on original Arcene dataset.")
      h2o_model_2 = H2OGeneralizedLinearEstimator(
          family="binomial", lambda_search=True, alpha=0.5
      )
      h2o_model_2.train(
          x=list(range(1000)), y=1000, training_frame=train_data
      )
  
      print("Create model on shuffled Arcene dataset.")
      h2o_model_s = H2OGeneralizedLinearEstimator(
          family="binomial", lambda_search=True, alpha=0.5
      )
      h2o_model_s.train(
          x=list(range(1000)),
          y=1000,
          training_frame=train_data_shuffled,
      )
  
      print(
          "Assert that number of predictors remaining and their respective coefficients are equal."
      )
  
      for x, y in zip(
          h2o_model._model_json["output"][
              "coefficients_table"
          ].cell_values,
          h2o_model_2._model_json["output"][
              "coefficients_table"
          ].cell_values,
      ):
          assert (type(x[1]) == type(y[1])) and (
              type(x[2]) == type(y[2])
          ), "coefficients should be the same type"
          if isinstance(x[1], float):
              assert (
                  abs(x[1] - y[1]) < 5e-10
              ), "coefficients should be equal"
          if isinstance(x[2], float):
              assert (
                  abs(x[2] - y[2]) < 5e-10
              ), "coefficients should be equal"
  
      for x, y in zip(
          h2o_model._model_json["output"][
              "coefficients_table"
          ].cell_values,
          h2o_model_s._model_json["output"][
              "coefficients_table"
          ].cell_values,
      ):
          assert (type(x[1]) == type(y[1])) and (
              type(x[2]) == type(y[2])
          ), "coefficients should be the same type"
          if isinstance(x[1], float):
              assert (
                  abs(x[1] - y[1]) < 5e-10
              ), "coefficients should be equal"
          if isinstance(x[2], float):
              assert (
                  abs(x[2] - y[2]) < 5e-10
              ), "coefficients should be equal"
  
imports: from builtins import zip
  from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: shuffling_large() without return types