code: def _process_operator(operator, operators, depth=0):
      steps = []
      op_name = operator[0]
  
      if op_name == "CombineDFs":
          steps.append(
              _combine_dfs(operator[1], operator[2], operators)
          )
      else:
          input_name, args = operator[1], operator[2:]
          tpot_op = get_by_name(op_name, operators)
  
          if input_name != "input_matrix":
              steps.extend(
                  _process_operator(input_name, operators, depth + 1)
              )
  
          # If the step is an estimator and is not the last step then we must
          # add its guess as synthetic feature(s)
          # classification prediction for both regression and classification
          # classification probabilities for classification if available
          if tpot_op.root and depth > 0:
              steps.append(
                  "StackingEstimator(estimator={})".format(
                      tpot_op.export(*args)
                  )
              )
          else:
              steps.append(tpot_op.export(*args))
      return steps
  
file_path: tpot/tpot/export_utils.py
imports: import deap
label_desc: Use image node_id 9 for calling a global function with example usage: _process_operator(operator, operators, depth) and returns: steps