code: def predict(self, data, useCPUOnly=False, **kwargs):
          
  
          if self.__proxy__:
              return self.__proxy__.predict(data,useCPUOnly)
          else:
              if _macos_version() < (10, 13):
                  raise Exception('Model prediction is only supported on macOS version 10.13 or later.')
  
              try:
                  from ..libcoremlpython import _MLModelProxy
              except:
                  _MLModelProxy = None
  
              if not _MLModelProxy:
                  raise Exception('Unable to load CoreML.framework. Cannot make predictions.')
              elif _MLModelProxy.maximum_supported_specification_version() < self._spec.specificationVersion:
                  engineVersion = _MLModelProxy.maximum_supported_specification_version()
                  raise Exception('The specification has version ' + str(self._spec.specificationVersion)
                                  + ' but the Core ML framework version installed only supports Core ML model specification version '
                                  + str(engineVersion) + ' or older.')
              elif _has_custom_layer(self._spec):
                  raise Exception('This model contains a custom neural network layer, so predict is not supported.')
              else:
                  raise Exception('Unable to load CoreML.framework. Cannot make predictions.')
docs: Return predictions for the model. The kwargs gets passed into the
          model as a dictionary.
  
          Parameters
          ----------
          data : dict[str, value]
              Dictionary of data to make predictions from where the keys are
              the names of the input features.
  
          useCPUOnly : bool
              Set to true to restrict computation to use only the CPU. Defaults to False.
  
          Returns
          -------
          out : dict[str, value]
              Predictions as a dictionary where each key is the output feature
              name.
  
          Examples
          --------
          >>> data = {'bedroom': 1.0, 'bath': 1.0, 'size': 1240}
          >>> predictions = model.predict(data)