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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict(self, data, useCPUOnly=False, **kwargs):
 """ 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 -------- """


        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.')