def visualize_spec(self, port=None, input_shape_dict=None):
        """
            Visualize the model.

            Parameters
            ----------
            port : int
                if server is to be hosted on specific localhost port

            input_shape_dict : dict
                The shapes are calculated assuming the batch and sequence
                are 1 i.e. (1, 1, C, H, W). If either is not 1, then provide
                full input shape

            Returns
            -------

            None

            Examples
            --------
            >>> model = coreml.models.MLModel('HousePricer.mlmodel')
            >>> model.visualize_spec()
            """

        spec = self._spec
        model_type = spec.WhichOneof('Type')
        model_description = spec.description
        input_spec = model_description.input
        output_spec = model_description.output

        spec_inputs = []
        for model_input in input_spec:
            spec_inputs.append((model_input.name, str(model_input.type)))

        spec_outputs = []
        for model_output in output_spec:
            spec_outputs.append((model_output.name, str(model_output.type)))

        cy_nodes = []
        cy_edges = []

        cy_nodes.append({
            'data': {
                'id': 'input_node',
                'name': '',
                'info': {
                    'type': 'input node'
                },
                'classes': 'input',

            }
        })

        for model_input, input_type in spec_inputs:
            cy_nodes.append({
                'data': {
                    'id': str(model_input),
                    'name': str(model_input),
                    'info': {
                        'type': "\n".join(str(input_type).split("\n")),
                        'inputs': str([]),
                        'outputs': str([model_input])
                    },
                    'parent': 'input_node'
                },
                'classes': 'input'
            })

        if model_type == 'pipeline':
            pipeline_spec = spec.pipeline
            cy_data = _pipeline_nodes_and_edges(cy_nodes,
                                                cy_edges,
                                                pipeline_spec,
                                                spec_outputs
                                                )
        elif model_type == 'pipelineRegressor':
            pipeline_spec = spec.pipelineRegressor.pipeline
            cy_data = _pipeline_nodes_and_edges(cy_nodes,
                                                cy_edges,
                                                pipeline_spec,
                                                spec_outputs
                                                )
        elif model_type == 'pipelineClassifier':
            pipeline_spec = spec.pipelineClassifier.pipeline
            cy_data = _pipeline_nodes_and_edges(cy_nodes,
                                                cy_edges,
                                                pipeline_spec,
                                                spec_outputs
                                                )
        elif model_type == 'neuralNetwork':
            nn_spec = spec.neuralNetwork
            cy_data = _neural_network_nodes_and_edges(nn_spec,
                                                      cy_nodes,
                                                      cy_edges,
                                                      spec_outputs,
                                                      input_spec,
                                                      input_shape_dict=input_shape_dict
                                                      )
        elif model_type == 'neuralNetworkClassifier':
            nn_spec = spec.neuralNetworkClassifier
            cy_data = _neural_network_nodes_and_edges(nn_spec,
                                                      cy_nodes,
                                                      cy_edges,
                                                      spec_outputs,
                                                      input_spec,
                                                      input_shape_dict=input_shape_dict
                                                      )
        elif model_type == 'neuralNetworkRegressor':
            nn_spec = spec.neuralNetworkRegressor
            cy_data = _neural_network_nodes_and_edges(nn_spec,
                                                      cy_nodes,
                                                      cy_edges,
                                                      spec_outputs,
                                                      input_spec,
                                                      input_shape_dict=input_shape_dict
                                                      )
        else:
            print("Model is not of type Pipeline or Neural Network "
                  "and cannot be visualized")
            return


        import coremltools
        web_dir = _os.path.join(_os.path.dirname(coremltools.__file__),
                                'graph_visualization')
        with open('{}/model.json'.format(web_dir), 'w') as file:
            _json.dump(cy_data, file)

        _start_server(port, web_dir)
Visualize the model.

            Parameters
            ----------
            port : int
                if server is to be hosted on specific localhost port

            input_shape_dict : dict
                The shapes are calculated assuming the batch and sequence
                are 1 i.e. (1, 1, C, H, W). If either is not 1, then provide
                full input shape

            Returns
            -------

            None

            Examples
            --------
            >>> model = coreml.models.MLModel('HousePricer.mlmodel')
            >>> model.visualize_spec()