code: def _get_output_feature_names(
      transformer, num_features, input_features
  ):
      import sklearn
  
      # `get_feature_names_out` was introduced in scikit-learn 1.0.0.
      if Version(sklearn.__version__) < Version("1.0.0"):
          return _generate_feature_names(num_features)
  
      try:
          # `get_feature_names_out` fails if `transformer` contains a transformer that doesn't
          # implement `get_feature_names_out`. For example, `FunctionTransformer` only implements
          # `get_feature_names_out` when it's instantiated with `feature_names_out`.
          # In scikit-learn >= 1.1.0, all transformers implement `get_feature_names_out`.
          # In scikit-learn == 1.0.*, some transformers implement `get_feature_names_out`.
          return transformer.get_feature_names_out(input_features)
      except Exception as e:
          _logger.warning(
              f"Failed to get output feature names with `get_feature_names_out`: {e}. "
              "Falling back to using auto-generated feature names."
          )
          return _generate_feature_names(num_features)
  
imports: import importlib
  import logging
  import os
  import sys
  import time
  import cloudpickle
  from packaging.version import Version
  from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.recipes.artifacts import DataframeArtifact, TransformerArtifact
  from mlflow.recipes.cards import BaseCard
  from mlflow.recipes.step import BaseStep, StepClass
  from mlflow.recipes.utils.execution import get_step_output_path
  from mlflow.recipes.utils.step import get_pandas_data_profiles, validate_classification_config
  from mlflow.recipes.utils.tracking import TrackingConfig, get_recipe_tracking_config
label_desc: Use image node_id 2 for calling a global function with example usage: _get_output_feature_names(transformer, num_features, input_features) and returns: _generate_feature_names, transformer, _generate_feature_names