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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 _parse_config(self, config):
 """ Parses a tensorflow configuration """

        self._batch_size = config['batch_size']
        self._im_height = config['im_height']
        self._im_width = config['im_width']
        self._num_channels = config['channels']
        self._output_layer = config['out_layer']
        self._feature_layer = config['feature_layer']
        self._out_size = None
        if 'out_size' in config.keys():
            self._out_size = config['out_size']
        self._input_arr = np.zeros([self._batch_size, self._im_height,
                                    self._im_width, self._num_channels])

        if self._model_dir is None:
            self._net_data = np.load(config['caffe_weights']).item()
            self._mean = np.load(config['mean_file'])
            self._model_filename = None
        else:
            self._net_data = None
            self._mean = np.load(os.path.join(self._model_dir, 'mean.npy'))
            self._model_filename = os.path.join(self._model_dir, 'model.ckpt')