code: def random_dataset(response_type="regression", verbose=True):
      """Create and return a random dataset."""
      if verbose:
          print(
              "\nCreating a dataset for a %s problem:" % response_type
          )
      fractions = {
          k + "_fraction": 0.5
          for k in "real categorical integer time string binary".split()
      }
      fractions[
          "string_fraction"
      ] = 0  # Right now we are dropping string columns, so no point in having them.
      fractions["binary_fraction"] /= 3
      fractions["time_fraction"] /= 2
  
      sum_fractions = sum(fractions.values())
      for k in fractions:
          fractions[k] /= sum_fractions
      response_factors = (
          1
          if response_type == "regression"
          else 2
          if response_type == "binomial"
          else random.randint(3, 10)
      )
      df = h2o.create_frame(
          rows=5000 + NTESTROWS,
          cols=5,
          missing_fraction=0.025,
          has_response=True,
          response_factors=response_factors,
          positive_response=True,
          factors=10,
          seed=1234,
          **fractions,
      )
      if verbose:
          print()
          df.show()
      return df
  
file_path: h2o-3/h2o-py/tests/testdir_javapredict/pyunit_pubdev_4628_pubdev_4950_deeplearning_mojo_pojo_autoencoder_NOPASS.py
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  import random
  import re
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 6 for calling a global function with example usage: random_dataset(response_type, verbose) and returns: df