class_docstr: NT-Xent loss for SimCLR Self-Supervised learning approach -
  https://arxiv.org/abs/2002.05709
  
  Args:
      temperature (float): scalar value to scale the loss by.
docstr: Args:
      x_list (list[torch.tensor]): A list of two tensors of shape N x C.
          Where, N is the batch size and C is the SSL model's embedding size.
docstr_tok: ["Args",":","x_list","(","list","[","torch.tensor","]",")",":","A","list","of","two","tensors","of","shape","N","x","C.","Where",",","N","is","the","batch","size","and","C","is","the","SSL","model","'s","embedding","size","."]
code: def forward(self, x_list: List[torch.Tensor]) -> torch.Tensor:
      """
      Args:
          x_list (list[torch.tensor]): A list of two tensors of shape N x C.
              Where, N is the batch size and C is the SSL model's embedding size.
      """
      assert (
          len(x_list) == 2
      ), f"Invalid list input to SimCLR. Expected dimention 2 but received {len(x_list)}"
  
      out_1, out_2 = x_list
  
      if (
          torch.distributed.is_available()
          and torch.distributed.is_initialized()
      ):
          out_1 = du.AllGatherWithGradient.apply(
              out_1
          )  # pyre-ignore[16]
          out_2 = du.AllGatherWithGradient.apply(out_2)
      out = torch.cat([out_1, out_2], dim=0)
      # [2*B, 2*B]
      sim_matrix = torch.exp(
          torch.mm(out, out.t().contiguous()) / self.temperature
      )
      mask = (
          torch.ones_like(sim_matrix)
          - torch.eye(out.shape[0], device=sim_matrix.device)
      ).bool()
      # [2*B, 2*B-1]
      sim_matrix = sim_matrix.masked_select(mask).view(out.shape[0], -1)
      # compute loss
      pos_sim = torch.exp(
          torch.sum(out_1 * out_2, dim=-1) / self.temperature
      )
      # [2*B]
      pos_sim = torch.cat([pos_sim, pos_sim], dim=0)
      loss = (-torch.log(pos_sim / sim_matrix.sum(dim=-1))).mean()
  
      return loss
  
imports: from typing import List
  import pytorchvideo_trainer.module.distributed_utils
  import torch
  import torch.nn
  import torch.nn.functional
  from pytorchvideo.layers.utils import set_attributes
label_desc: Use image node_id 2 for calling the NtxentLoss obj's underlying member method code with example usage: obj.forward(x_list) and returns: loss