code: def load_label(self, idx):
          
          label_400 = scipy.io.loadmat('{}/trainval/{}.mat'.format(self.context_dir, idx))['LabelMap']
          label = np.zeros_like(label_400, dtype=np.uint8)
          for idx, l in enumerate(self.labels_59):
              idx_400 = self.labels_400.index(l) + 1
              label[label_400 == idx_400] = idx + 1
          label = label[np.newaxis, ...]
          return label
docs: Load label image as 1 x height x width integer array of label indices.
          The leading singleton dimension is required by the loss.
          The full 400 labels are translated to the 59 class task labels.