code: def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
      """Convert tensor to 3-channel images.
  
      Args:
          tensor (torch.Tensor): Tensor that contains multiple images, shape (
              N, C, H, W).
          mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
          std (tuple[float], optional): Standard deviation of images.
              Defaults to (1, 1, 1).
          to_rgb (bool, optional): Whether the tensor was converted to RGB
              format in the first place. If so, convert it back to BGR.
              Defaults to True.
  
      Returns:
          list[np.ndarray]: A list that contains multiple images.
      """
  
      if torch is None:
          raise RuntimeError("pytorch is not installed")
      assert torch.is_tensor(tensor) and tensor.ndim == 4
      assert len(mean) == 3
      assert len(std) == 3
  
      num_imgs = tensor.size(0)
      mean = np.array(mean, dtype=np.float32)
      std = np.array(std, dtype=np.float32)
      imgs = []
      for img_id in range(num_imgs):
          img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
          img = mmcv.imdenormalize(
              img, mean, std, to_bgr=to_rgb
          ).astype(np.uint8)
          imgs.append(np.ascontiguousarray(img))
      return imgs
  
label_desc: Use image node_id 1 for calling a global function with example usage: tensor2imgs(tensor, mean, std, to_rgb) and returns: imgs