class_docstr: Temperature-scaled Similarity loss for BYOL Self-Supervised learning
  approach - https://arxiv.org/abs/2006.07733
  
  Args:
      temperature (float): scalar value to scale the loss by.
docstr: Args:
      q and k (nn.tensor): inputs to calculate the similarity, expected to have
          the same shape of `N x C`. Where N is the batch size and C
          is the SSL model's embedding size.
docstr_tok: ["Args",":","q","and","k","(","nn.tensor",")",":","inputs","to","calculate","the","similarity",",","expected","to","have","the","same","shape","of","`","N","x","C","`",".","Where","N","is","the","batch","size","and","C","is","the","SSL","model","'s","embedding","size","."]
code: def forward(self, q: torch.Tensor, k: torch.Tensor) -> torch.Tensor:
      """
      Args:
          q and k (nn.tensor): inputs to calculate the similarity, expected to have
              the same shape of `N x C`. Where N is the batch size and C
              is the SSL model's embedding size.
      """
      similarity = torch.einsum("nc,nc->n", [q, k])
      similarity /= self.temperature
      loss = -similarity.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 SimilarityLoss obj's underlying member method code with example usage: obj.forward(q, k) and returns: loss