class_docstr: Helper class to create model test data, setup and run experiment.
  
  Contain the needed model experiment statistics as class attributes.
class_docstr_tok: ["Helper","class","to","create","model","test","data",",","setup","and","run","experiment",".","Contain","the","needed","model","experiment","statistics","as","class","attributes","."]
docstr: Configure and setup test model.
docstr_tok: ["Configure","and","setup","test","model","."]
code: def _create_model(self):
      """Configure and setup test model."""
      config = {
          "input_features": self.input_features,
          "output_features": self.output_features,
          "combiner": {"type": "concat", "output_size": 14},
          TRAINER: {"epochs": 2, BATCH_SIZE: 128},
      }
      return LudwigModel(config, logging_level=logging.WARN)
  
imports: import glob
  import logging
  import os
  from tempfile import TemporaryDirectory
  import numpy
  import pytest
  from ludwig import visualize
  from ludwig.api import LudwigModel, TrainingStats
  from ludwig.constants import BATCH_SIZE, ENCODER, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, TRAINER, TYPE
  from ludwig.data.split import get_splitter
  from ludwig.globals import HYPEROPT_STATISTICS_FILE_NAME
  from ludwig.utils.data_utils import read_csv
  from tests.integration_tests.utils import bag_feature, binary_feature, category_feature, generate_data, LocalTestBackend, number_feature, sequence_feature, set_feature, text_feature
label_desc: Use image node_id 2 for calling the Experiment obj's underlying member method code with example usage: obj._create_model() and returns: LudwigModel