code: def _starting_imports(operators, operators_used, pipeline_module):
      # number of operators
      num_op = len(operators_used)
  
      # number of classifier/regressor or CombineDFs
      num_op_root = 0
      for op in operators_used:
          if op != "CombineDFs":
              tpot_op = get_by_name(op, operators)
              if tpot_op.root:
                  num_op_root += 1
          else:
              num_op_root += 1
  
      if num_op_root > 1:
          if pipeline_module == "sklearn":
              return {
                  "sklearn.model_selection": ["train_test_split"],
                  "sklearn.pipeline": ["make_union", "make_pipeline"],
                  "tpot.builtins": ["StackingEstimator"],
              }
          else:
              {
                  "sklearn.model_selection": ["train_test_split"],
                  "sklearn.pipeline": ["make_union"],
                  "imblearn.pipeline": ["make_pipeline"],
                  "tpot.builtins": ["StackingEstimator"],
              }
      elif num_op > 1:
          return {
              "sklearn.model_selection": ["train_test_split"],
              f"{pipeline_module}.pipeline": ["make_pipeline"],
          }
      # if operators # == 1 and classifier/regressor # == 1, this import statement is simpler
      else:
          return {"sklearn.model_selection": ["train_test_split"]}
  
file_path: tpot/tpot/export_utils.py
imports: import deap
label_desc: Use image node_id 5 for calling a global function with example usage: _starting_imports(operators, operators_used, pipeline_module) and returns: dict, dict, dict