code: def export_pipeline(
      exported_pipeline,
      operators,
      pset,
      impute=False,
      pipeline_score=None,
      random_state=None,
      data_file_path="",
  ):
      """Generate source code for a TPOT Pipeline.
  
      Parameters
      ----------
      exported_pipeline: deap.creator.Individual
          The pipeline that is being exported
      operators:
          List of operator classes from operator library
      pipeline_score:
          Optional pipeline score to be saved to the exported file
      impute: bool (False):
          If impute = True, then adda a imputation step.
      random_state: integer
          Random seed in train_test_split function and exported pipeline.
      data_file_path: string (default: '')
          By default, the path of input dataset is 'PATH/TO/DATA/FILE' by default.
          If data_file_path is another string, the path will be replaced.
  
      Returns
      -------
      pipeline_text: str
          The source code representing the pipeline
  
      """
      # Unroll the nested function calls into serial code
      pipeline_tree = expr_to_tree(exported_pipeline, pset)
  
      # Have the exported code import all of the necessary modules and functions
      pipeline_text = generate_import_code(
          exported_pipeline, operators, impute, random_state
      )
  
      pipeline_code = pipeline_code_wrapper(
          generate_export_pipeline_code(pipeline_tree, operators),
          random_state,
      )
  
      if pipeline_code.count("FunctionTransformer(copy)"):
          pipeline_text += """from sklearn.preprocessing import FunctionTransformer
  from copy import copy
  """
      if not data_file_path:
          data_file_path = "PATH/TO/DATA/FILE"
  
      pipeline_text += """
  # NOTE: Make sure that the outcome column is labeled 'target' in the data file
  tpot_data = pd.read_csv('{}', sep='COLUMN_SEPARATOR', dtype=np.float64)
  features = tpot_data.drop('target', axis=1)
  training_features, testing_features, training_target, testing_target = \\
              train_test_split(features, tpot_data['target'], random_state={})
  """.format(
          data_file_path, random_state
      )
  
      # Add the imputation step if it was used by TPOT
      if impute:
          pipeline_text += """
  imputer = SimpleImputer(strategy="median")
  imputer.fit(training_features)
  training_features = imputer.transform(training_features)
  testing_features = imputer.transform(testing_features)
  """
  
      if pipeline_score is not None:
          pipeline_text += (
              "\n# Average CV score on the training set was: {}".format(
                  pipeline_score
              )
          )
      pipeline_text += "\n"
  
      # Replace the function calls with their corresponding Python code
      pipeline_text += pipeline_code
  
      return pipeline_text
  
file_path: tpot/tpot/export_utils.py
imports: import deap
label_desc: Use image node_id 2 for calling a global function with example usage: export_pipeline(exported_pipeline, operators, pset, impute, pipeline_score, random_state, data_file_path) and returns: pipeline_text