class_docstr: Compute multiple mse losses and return their average.
class_docstr_tok: ["Compute","multiple","mse","losses","and","return","their","average","."]
code: def forward(self, x, y):
      loss_sum = 0.0
      multi_loss = []
      for xt, yt in zip(x, y):
          if isinstance(yt, (tuple,)):
              if len(yt) == 2:
                  yt, wt = yt
                  lt = "mse"
              elif len(yt) == 3:
                  yt, wt, lt = yt
              else:
                  raise NotImplementedError
          else:
              wt, lt = 1.0, "mse"
          if lt == "mse":
              loss = self.mse_func(xt, yt)
          else:
              raise NotImplementedError
          loss_sum += loss * wt
          multi_loss.append(loss)
      return loss_sum, multi_loss
  
file_path: SlowFast/slowfast/models/losses.py
imports: from functools import partial
  import torch
  import torch.nn
  from pytorchvideo.losses.soft_target_cross_entropy import SoftTargetCrossEntropyLoss
label_desc: Use image node_id 2 for calling the MultipleMSELoss obj's underlying member method code with example usage: obj.forward(x, y) and returns: loss_sum,  multi_loss