code: def _parse_label(self, label):
          
          if isinstance(label, nd.NDArray):
              label = label.asnumpy()
          raw = label.ravel()
          if raw.size < 7:
              raise RuntimeError("Label shape is invalid: " + str(raw.shape))
          header_width = int(raw[0])
          obj_width = int(raw[1])
          if (raw.size - header_width) % obj_width != 0:
              msg = "Label shape %s inconsistent with annotation width %d." \
                  %(str(raw.shape), obj_width)
              raise RuntimeError(msg)
          out = np.reshape(raw[header_width:], (-1, obj_width))
          # remove bad ground-truths
          valid = np.where(np.logical_and(out[:, 3] > out[:, 1], out[:, 4] > out[:, 2]))[0]
          if valid.size < 1:
              raise RuntimeError('Encounter sample with no valid label.')
          return out[valid, :]
docs: Helper function to parse object detection label.
  
          Format for raw label:
          n \t k \t ... \t [id \t xmin\t ymin \t xmax \t ymax \t ...] \t [repeat]
          where n is the width of header, 2 or larger
          k is the width of each object annotation, can be arbitrary, at least 5