def __init__(self, network_graph_filename, plugin=None,
                 prob_thresh=0.0, classification_mask=None):
        """Initialize an instance of the class.

        :param network_graph_filename: the file path and name of the graph file created by the OpenVINO compiler
        :param nc_device: an open neural compute device object to use for inferences for this graph file
        :param prob_thresh: the probability threshold (between 0.0 and 1.0)... results below this threshold will be
        excluded
        :param classification_mask: a list of 0/1 values, one for each classification label in the
        _classification_mask list... if the value is 0 then the corresponding classification won't be reported.
        :return : None

        """
        # Load graph from disk and allocate graph to device
        try:
            
            self._net = IENetwork(model=network_graph_filename + ".xml", weights=network_graph_filename + ".bin")
            self._input_blob = next(iter(self._net.inputs))
            self._output_blob = next(iter(self._net.outputs))
            self._exec_net = plugin.load(network=self._net)
            #print("network shape: ",self._net.inputs[self._input_blob].shape)
            self._n, self._input_total_size = self._net.inputs[self._input_blob].shape
            del self._net


        except IOError as e:
            print('Error - could not load neural network graph file: ' + network_graph_filename)
            raise e

        # If no mask was passed then create one to accept all classifications
        self._classification_mask = classification_mask if classification_mask else [1] * 10
        self._probability_threshold = prob_thresh
        self._end_flag = True
Initialize an instance of the class.

        :param network_graph_filename: the file path and name of the graph file created by the OpenVINO compiler
        :param nc_device: an open neural compute device object to use for inferences for this graph file
        :param prob_thresh: the probability threshold (between 0.0 and 1.0)... results below this threshold will be
        excluded
        :param classification_mask: a list of 0/1 values, one for each classification label in the
        _classification_mask list... if the value is 0 then the corresponding classification won't be reported.
        :return : None

        
apps/mnist_calc/mnist_processor.py