class_docstr: ``nn.Module`` wrapper for ``pytorchvideo.transforms.functional.div_255``.
class_docstr_tok: ["``","nn.Module","``","wrapper","for","``","pytorchvideo.transforms.functional.div_255","``","."]
docstr: Scale clip frames from [0, 255] to [0, 1].
  Args:
      x (Tensor): A tensor of the clip's RGB frames with shape:
          (C, T, H, W).
  Returns:
      x (Tensor): Scaled tensor by dividing 255.
docstr_tok: ["Scale","clip","frames","from","[","0",",","255","]","to","[","0",",","1","]",".","Args",":","x","(","Tensor",")",":","A","tensor","of","the","clip","'s","RGB","frames","with","shape",":","(","C",",","T",",","H",",","W",")",".","Returns",":","x","(","Tensor",")",":","Scaled","tensor","by","dividing","255","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Scale clip frames from [0, 255] to [0, 1].
      Args:
          x (Tensor): A tensor of the clip's RGB frames with shape:
              (C, T, H, W).
      Returns:
          x (Tensor): Scaled tensor by dividing 255.
      """
      return torchvision.transforms.Lambda(
          pytorchvideo.transforms.functional.div_255
      )(x)
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 1 for calling the Div255 obj's underlying member method code with example usage: obj.forward(x) and returns: torchvision