code: def merge_dict_with_features(
      a: ModelConfigDict, b: ModelConfigDict
  ) -> ModelConfigDict:
      merge_lists(a[INPUT_FEATURES], b.get(INPUT_FEATURES, []))
      merge_lists(a[OUTPUT_FEATURES], b.get(OUTPUT_FEATURES, []))
  
      b = b.copy()
      if INPUT_FEATURES in b:
          del b[INPUT_FEATURES]
      if OUTPUT_FEATURES in b:
          del b[OUTPUT_FEATURES]
  
      return merge_dict(a, b)
  
imports: import os
  import tempfile
  from typing import List, Set
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from ludwig.api import LudwigModel
  from ludwig.constants import COLUMN, ENCODER, INPUT_FEATURES, NAME, OUTPUT_FEATURES, PREPROCESSING, SPLIT, TYPE
  from ludwig.schema.model_types.base import ModelConfig
  from ludwig.types import FeatureConfigDict, ModelConfigDict
  from ludwig.utils.misc_utils import merge_dict
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, image_feature, minio_test_creds, number_feature, private_param, remote_tmpdir, text_feature
  import dask.dataframe
  from ray.tune.experiment.trial import Trial
  from ludwig.automl import auto_train, create_auto_config, train_with_config
  from ludwig.automl.automl import OUTPUT_DIR
  from ludwig.hyperopt.execution import RayTuneExecutor
label_desc: Use image node_id 3 for calling a global function with example usage: merge_dict_with_features(a, b) and returns: merge_dict