code: def load_model_with_output(
      model_path=None,
      model_name=None,
      outputs=None,
      postprocessors=None,
      **kwargs,
  ):
      if model_path is not None:
          model = load_old_model(model_path, **kwargs)
  
      elif model_name is not None:
          model = load_old_model(load(model_name), **kwargs)
  
      else:
          print("Error. No model provided.")
          return
  
      for output in outputs:
          model.append_output([output])
  
          for postprocessor in postprocessors:
              output.append_postprocessor([postprocessor])
  
      return model
  
imports: import csv
  from deepneuro.models.cost_functions import cost_function_dict
  from deepneuro.utilities.util import add_parameter, nifti_splitext
  from deepneuro.load.load import load
label_desc: Use image node_id 2 for calling a global function with example usage: load_model_with_output(model_path, model_name, outputs, postprocessors) and returns: model