code: def train_and_evaluate(
      config: ModelConfigDict, dataset: pd.DataFrame
  ):
      """Trains and evaluates a model with the given config.
  
      Args:
          config: valid Ludwig config.
          dataset: Ludwig dataset name to train on.
      """
      # adding print statements to be captured in pytest stdout and help debug tests.
      print("Config used (trained on synthetic data)")
      pprint(config)
      model = LudwigModel(
          config=config, callbacks=None, logging_level=logging.ERROR
      )
      model.train(dataset=dataset)
      model.evaluate(dataset=dataset)
  
imports: import logging
  from collections import deque
  from pprint import pprint
  from typing import Any, Dict, Tuple
  import pandas
  import pytest
  import yaml
  from ludwig.api import LudwigModel
  from ludwig.config_sampling.explore_schema import combine_configs, ConfigOption, explore_properties
  from ludwig.config_validation.validation import get_schema
  from ludwig.types import ModelConfigDict
  from .configs import COMBINER_TYPE_TO_COMBINE_FN_MAP, ECD_CONFIG_SECTION_TO_CONFIG, FEATURE_TYPE_TO_CONFIG_FOR_DECODER_LOSS, FEATURE_TYPE_TO_CONFIG_FOR_ENCODER_PREPROCESSING
label_desc: Use image node_id 4 for calling a global function with example usage: train_and_evaluate(config, dataset) without return types