class_docstr: Bootstrap Your Own Latent A New Approach to Self-Supervised Learning
  Details can be found in:
  https://arxiv.org/pdf/2006.07733.pdf
docstr: Forward momentum backbone.
  Args:
      x (tensor): input to be forwarded.
docstr_tok: ["Forward","momentum","backbone",".","Args",":","x","(","tensor",")",":","input","to","be","forwarded","."]
code: def forward_backbone_mmt(self, x):
      """
      Forward momentum backbone.
      Args:
          x (tensor): input to be forwarded.
      """
      with torch.no_grad():
          proj = self.backbone_mmt(x)
      return F.normalize(proj, dim=1)
  
file_path: pytorchvideo/pytorchvideo/models/byol.py
imports: import copy
  from typing import Callable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 6 for calling the BYOL obj's underlying member method code with example usage: obj.forward_backbone_mmt(x) and returns: F