def predict(self, data, state: _Optional[MLState] = None):
        """
        Return predictions for the model.

        Parameters
        ----------
        data: dict[str, value] or list[dict[str, value]]
            Dictionary of data to use for predictions, where the keys are the names of the input features.
            For batch predictons, use a list of such dictionaries.

            The following dictionary values types are acceptable: list, array, numpy.ndarray, tensorflow.Tensor
            and torch.Tensor.

        state : MLState
            Optional state object as returned by ``make_state()``.

        Returns
        -------
        dict[str, value]
            Predictions as a dictionary where each key is the output feature name.

        list[dict[str, value]]
            For batch prediction, returns a list of the above dictionaries.

        Examples
        --------
        .. sourcecode:: python

            data = {"bedroom": 1.0, "bath": 1.0, "size": 1240}
            predictions = model.predict(data)

            data = [
                {"bedroom": 1.0, "bath": 1.0, "size": 1240},
                {"bedroom": 4.0, "bath": 2.5, "size": 2400},
            ]
            batch_predictions = model.predict(data)

        """
        def verify_and_convert_input_dict(d):
            self._verify_input_dict(d)
            self._convert_tensor_to_numpy(d)
            # TODO: remove the following call when this is fixed: rdar://92239209
            self._update_float16_multiarray_input_to_float32(d)

        if self.is_package and _is_macos() and _macos_version() < (12, 0):
            raise Exception(
                "predict() for .mlpackage is not supported in macOS version older than 12.0."
            )
        MLModel._check_predict_data(data)

        if self.__proxy__:
            return self._get_predictions(self.__proxy__,
                                         verify_and_convert_input_dict,
                                         data,
                                         state)
        else:   # Error case
            if _macos_version() < (10, 13):
                raise Exception(
                    "Model prediction is only supported on macOS version 10.13 or later."
                )

            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:
                if self._framework_error:
                    raise self._framework_error
                else:
                    raise Exception("Unable to load CoreML.framework. Cannot make predictions.")

        Return predictions for the model.

        Parameters
        ----------
        data: dict[str, value] or list[dict[str, value]]
            Dictionary of data to use for predictions, where the keys are the names of the input features.
            For batch predictons, use a list of such dictionaries.

            The following dictionary values types are acceptable: list, array, numpy.ndarray, tensorflow.Tensor
            and torch.Tensor.

        state : MLState
            Optional state object as returned by ``make_state()``.

        Returns
        -------
        dict[str, value]
            Predictions as a dictionary where each key is the output feature name.

        list[dict[str, value]]
            For batch prediction, returns a list of the above dictionaries.

        Examples
        --------
        .. sourcecode:: python

            data = {"bedroom": 1.0, "bath": 1.0, "size": 1240}
            predictions = model.predict(data)

            data = [
                {"bedroom": 1.0, "bath": 1.0, "size": 1240},
                {"bedroom": 4.0, "bath": 2.5, "size": 2400},
            ]
            batch_predictions = model.predict(data)

        
coremltools/models/model.py