code: def _mae_visualize(self, imgs, pred, mask):
      N, T, H, W, p, u, t, h, w = self.patch_info
      pred = pred[0]
      if self.cfg.MASK.TIME_STRIDE_LOSS:
          im_viz = imgs[:, :, :: self.cfg.MVIT.PATCH_STRIDE[0], :, :]
      else:
          im_viz = imgs
      reconstruct = self._unpatchify(
          pred * mask.reshape(N, t * h * w, 1)
          + self._patchify(
              im_viz, time_stride_loss=self.cfg.MASK.TIME_STRIDE_LOSS
          )
          * (1 - mask.reshape(N, t * h * w, 1))
      )
      masked = self._unpatchify(
          self._patchify(
              im_viz, time_stride_loss=self.cfg.MASK.TIME_STRIDE_LOSS
          )
          * (1 - mask.reshape(N, t * h * w, 1))
      )
  
      comparison = torch.stack(
          [im_viz, masked, reconstruct],
          dim=1,
      ).permute([0, 1, 3, 2, 4, 5])
      pfx = self.cfg.TEST.CHECKPOINT_FILE_PATH
      mr = self.cfg.AUG.MASK_RATIO
      for i in range(comparison.shape[0]):
          misc.plot_input_normed(
              comparison[i].cpu(),
              bboxes=(),
              texts=(),
              path=self.cfg.OUTPUT_DIR
              + "/vis_mask/vid/{}vis_video_in_mask_out_mr{}/vis_{}_{}.mp4".format(
                  pfx[pfx.rfind("/") + 1 : -5], mr, self.counter, i
              ),
              folder_path=self.cfg.OUTPUT_DIR
              + "/vis_mask/vid/{}vis_video_in_mask_out_mr{}".format(
                  pfx[pfx.rfind("/") + 1 : -5], mr
              ),
              make_grids=True,
              output_video=True,
          )
      return pred[0]
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  import slowfast.utils.logging
  import slowfast.utils.misc
  from slowfast.models import head_helper
  from slowfast.models.attention import attention_pool
  from slowfast.models.utils import calc_mvit_feature_geometry
  from slowfast.models.video_model_builder import MViT
  from .None import head_helper, operators, resnet_helper, stem_helper
  from .build import MODEL_REGISTRY
label_desc: Use image node_id 15 for calling the MaskMViT obj's underlying member method code with example usage: obj._mae_visualize(imgs, pred, mask) and returns: pred