code: def preprocess(scopes, inputs):
      import warnings
      from .utils import parse_scopes
  
      if not isinstance(scopes, list):
          scopes = [scopes]
      outputs = []
      for scope in scopes:
          model_name = parse_scopes(scope)[0]
          try:
              outputs.append(__preprocess_dict__[model_name](inputs))
          except KeyError:
              found = False
              for key, fun in __preprocess_dict__.items():
                  if key in model_name.lower():
                      found = True
                      outputs.append(fun(inputs))
                      break
              if not found:
                  warnings.warn(
                      "No pre-processing will be performed "
                      "because the pre-processing for "
                      + model_name
                      + " are not found."
                  )
                  outputs.append(inputs)
      if len(outputs) == 1:
          outputs = outputs[0]
      return outputs
  
file_path: tensornets/tensornets/preprocess.py
imports: import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess(scopes, inputs) and returns: outputs