code: def get_default_feature_generator(
      feature_generator, feature_metadata=None, init_kwargs=None
  ):
      if init_kwargs is None:
          init_kwargs = dict()
      if feature_generator is None:
          feature_generator = IdentityFeatureGenerator()
      elif isinstance(feature_generator, str):
          if feature_generator == "auto":
              feature_generator = AutoMLPipelineFeatureGenerator(
                  **init_kwargs
              )
          elif feature_generator == "interpretable":
              feature_generator = (
                  AutoMLInterpretablePipelineFeatureGenerator(
                      **init_kwargs
                  )
              )
          else:
              raise ValueError(
                  f"Unknown feature_generator preset: '{feature_generator}', valid presets: {['auto', 'interpretable']}"
              )
      if feature_metadata is not None:
          if (
              feature_generator.feature_metadata_in is None
              and not feature_generator.is_fit()
          ):
              feature_generator.feature_metadata_in = copy.deepcopy(
                  feature_metadata
              )
          else:
              raise AssertionError(
                  "`feature_metadata_in` already exists in `feature_generator`."
              )
      return feature_generator
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_feature_generator(feature_generator, feature_metadata, init_kwargs) and returns: feature_generator