code: def h2ocreate_frame():
      """
      Python API test: h2o.create_frame(frame_id=None, rows=10000, cols=10, randomize=True, real_fraction=None,
       categorical_fraction=None, integer_fraction=None, binary_fraction=None, time_fraction=None,
        string_fraction=None, value=0, real_range=100, factors=100, integer_range=100,
        binary_ones_fraction=0.02, missing_fraction=0.01, has_response=False, response_factors=2,
        positive_response=False, seed=None, seed_for_column_types=None)
  
      Copied from pyunit_NOPASS_javapredict_dynamic_data_paramsDL.py
      """
      # Generate random dataset
      dataset_params = {}
      dataset_params["rows"] = random.sample(list(range(50, 150)), 1)[0]
      dataset_params["cols"] = random.sample(list(range(3, 6)), 1)[0]
      dataset_params["categorical_fraction"] = round(random.random(), 1)
      left_over = 1 - dataset_params["categorical_fraction"]
      dataset_params["integer_fraction"] = round(
          left_over - round(random.uniform(0, left_over), 1), 1
      )
      if (
          dataset_params["integer_fraction"]
          + dataset_params["categorical_fraction"]
          == 1
      ):
          if (
              dataset_params["integer_fraction"]
              > dataset_params["categorical_fraction"]
          ):
              dataset_params["integer_fraction"] = (
                  dataset_params["integer_fraction"] - 0.1
              )
          else:
              dataset_params["categorical_fraction"] = (
                  dataset_params["categorical_fraction"] - 0.1
              )
      dataset_params["missing_fraction"] = random.uniform(0, 0.5)
      dataset_params["has_response"] = False
      dataset_params["randomize"] = True
      dataset_params["factors"] = random.randint(2, 5)
      print("Dataset parameters: {0}".format(dataset_params))
  
      distribution = random.sample(
          ["bernoulli", "multinomial", "gaussian", "poisson", "gamma"],
          1,
      )[0]
      if distribution == "bernoulli":
          dataset_params["response_factors"] = 2
      elif distribution == "gaussian":
          dataset_params["response_factors"] = 1
      elif distribution == "multinomial":
          dataset_params["response_factors"] = random.randint(3, 5)
      else:
          dataset_params["has_response"] = False
      print("Distribution: {0}".format(distribution))
  
      train = h2o.create_frame(**dataset_params)
      assert_is_type(train, H2OFrame)
      assert (
          train.ncol == dataset_params["cols"]
      ), "h2o.create_frame() create frame with wrong column number."
      assert (
          train.nrow == dataset_params["rows"]
      ), "h2o.create_frame() create frame with wrong row number."
  
imports: import sys
  from tests import pyunit_utils
  import h2o
  import random
  from h2o.utils.typechecks import assert_is_type
  from h2o.frame import H2OFrame
label_desc: Use image node_id 1 for calling a global function with example usage: h2ocreate_frame() without return types