code: def check_error(model, path, shapes, output = 'softmax_output', verbose = True):
      
      coreml_model = _coremltools.models.MLModel(path)
      input_data = {}
      input_data_copy = {}
      for ip in shapes:
          input_data[ip] = _np.random.rand(*shapes[ip]).astype('f')
          input_data_copy[ip] = _np.copy(input_data[ip])
  
      dataIter = _mxnet.io.NDArrayIter(input_data_copy)
      mx_out = model.predict(dataIter).flatten()
  
      e_out_dict = coreml_model.predict(_mxnet_remove_batch(input_data))
      e_out = e_out_dict[output].flatten()
      error = _np.linalg.norm(e_out - mx_out)
  
      if verbose:
          print("First few predictions from CoreML : %s" % e_out[0:10])
          print("First few predictions from MXNet  : %s" % e_out[0:10])
          print("L2 Error on random data %s" % error)
      return error
docs: Check the difference between predictions from MXNet and CoreML.