class_docstr: Momentum Contrast for unsupervised Visual Representation Learning
  Details can be found in:
  https://arxiv.org/abs/1911.05722
class_docstr_tok: ["Momentum","Contrast","for","unsupervised","Visual","Representation","Learning","Details","can","be","found","in",":","https",":","\/\/arxiv.org\/abs\/1911.05722"]
docstr: Args:
      x (tensor): input to be forwarded of shape N x C x T x H x W
docstr_tok: ["Args",":","x","(","tensor",")",":","input","to","be","forwarded","of","shape","N","x","C","x","T","x","H","x","W"]
code: def forward(
      self, x: torch.Tensor
  ) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
      """
      Args:
          x (tensor): input to be forwarded of shape N x C x T x H x W
      """
      proj = self.backbone(x)
      out_proj = F.normalize(proj, dim=1)
      return out_proj
  
imports: import math
  from dataclasses import dataclass
  from typing import Any, Callable, List, Optional, Tuple, Union
  import pytorchvideo_trainer.module.distributed_utils
  import torch
  import torch.nn
  import torch.nn.functional
  from hydra.core.config_store import ConfigStore
  from omegaconf import MISSING
  from pytorchvideo.models.resnet import create_resnet
  from pytorchvideo.models.weight_init import init_net_weights
  from pytorchvideo_trainer.module.ssl_helper import create_mlp_util, SSLBaseModule
  from pytorchvideo_trainer.module.video_classification import Batch, BatchKey, EnsembleMethod
  from torchrecipes.core.conf import ModuleConf
  from torchrecipes.utils.config_utils import get_class_name_str
label_desc: Use image node_id 5 for calling the MOCO obj's underlying member method code with example usage: obj.forward(x) and returns: out_proj