def add_load_constant(self, name, output_name, constant_value, shape):
        """
        Add a load constant layer.
        Refer to the ``LoadConstantLayerParams`` message in the specification (NeuralNetwork.proto) for more details.

        Parameters
        ----------
        name: str
            The name of this layer.

        output_name: str
            The output blob name of this layer.

        constant_value: numpy.array
            value of the constant as a numpy array.

        shape: list of int or tuple of int
            List of ints representing the shape of the constant. Must be of length 3: [C,H,W]


        See Also
        --------
        add_elementwise
        """
        spec_layer = self._add_generic_layer(name, [], [output_name])
        spec_layer_params = spec_layer.loadConstant

        data = spec_layer_params.data
        data.floatValue.extend(constant_value.flatten())

        spec_layer_params.shape.extend(shape)

        self.rank_dict[output_name] = 5
        if len(data.floatValue) != _np.prod(shape):
            raise ValueError(
                "Dimensions of 'shape' do not match the size of the provided constant"
            )
        if not self._disable_rank5_shape_mapping:
            if len(shape) != 3:
                raise ValueError("'shape' must be of length 3")
        return spec_layer

        Add a load constant layer.
        Refer to the ``LoadConstantLayerParams`` message in the specification (NeuralNetwork.proto) for more details.

        Parameters
        ----------
        name: str
            The name of this layer.

        output_name: str
            The output blob name of this layer.

        constant_value: numpy.array
            value of the constant as a numpy array.

        shape: list of int or tuple of int
            List of ints representing the shape of the constant. Must be of length 3: [C,H,W]


        See Also
        --------
        add_elementwise
        
coremltools/models/neural_network/builder.py