code: def _declare_output_variables(
      raw_model_container, extra_config, outputs
  ):
      # Declare output variables.
      # Use the output names specified by the user, if any
      if constants.OUTPUT_NAMES in extra_config:
          assert len(extra_config[constants.OUTPUT_NAMES]) == len(
              outputs
          )
          for i in range(len(outputs)):
              outputs[i].raw_name = extra_config[
                  constants.OUTPUT_NAMES
              ][i]
  
      # The object raw_model_container is a part of the topology we're going to return.
      # We use it to store the outputs of the Sklearn/Spark-ML's computational graph.
      for variable in outputs:
          raw_model_container.add_output(variable)
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import pprint
  from uuid import uuid4
  import numpy
  from onnxconverter_common.optimizer import LinkedNode, _topological_sort
  from sklearn import pipeline
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import BaggingClassifier, BaggingRegressor
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.multioutput import MultiOutputRegressor, RegressorChain
  from sklearn.preprocessing import OneHotEncoder
  from .containers import CommonSklearnModelContainer, CommonONNXModelContainer, CommonSparkMLModelContainer
  from ._topology import Topology
  from ._utils import sklearn_installed, sparkml_installed
  from .operator_converters import constants
  from .supported import get_sklearn_api_operator_name, get_onnxml_api_operator_name, get_sparkml_api_operator_name
label_desc: Use image node_id 5 for calling a global function with example usage: _declare_output_variables(raw_model_container, extra_config, outputs) without return types