class_docstr: Compute multiple mse losses and return their average.
class_docstr_tok: ["Compute","multiple","mse","losses","and","return","their","average","."]
docstr: Args:
      reduction (str): specifies reduction to apply to the output. It can be
          "mean" (default) or "none".
docstr_tok: ["Args",":","reduction","(","str",")",":","specifies","reduction","to","apply","to","the","output",".","It","can","be","``","mean","''","(","default",")","or","``","none","''","."]
code: def __init__(self, reduction="mean"):
      """
      Args:
          reduction (str): specifies reduction to apply to the output. It can be
              "mean" (default) or "none".
      """
      super(MultipleMSELoss, self).__init__()
      self.mse_func = nn.MSELoss(reduction=reduction)
  
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 1 to create a new MultipleMSELoss object from inherited base classes: nn with example: obj = MultipleMSELoss(reduction)