class_docstr: Computes the MSE loss between the computed sentence embedding and a target sentence embedding. This loss
  is used when extending sentence embeddings to new languages as described in our publication
  Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation: https://arxiv.org/abs/2004.09813
  
  For an example, see the documentation on extending language models to new languages.
class_docstr_tok: ["Computes","the","MSE","loss","between","the","computed","sentence","embedding","and","a","target","sentence","embedding",".","This","loss","is","used","when","extending","sentence","embeddings","to","new","languages","as","described","in","our","publication","Making","Monolingual","Sentence","Embeddings","Multilingual","using","Knowledge","Distillation",":","https",":","\/\/arxiv.org\/abs\/2004.09813","For","an","example",",","see","the","documentation","on","extending","language","models","to","new","languages","."]
code: def forward(
      self,
      sentence_features: Iterable[Dict[str, Tensor]],
      labels: Tensor,
  ):
      rep = self.model(sentence_features[0])["sentence_embedding"]
      return self.loss_fct(rep, labels)
  
imports: from torch import nn, Tensor
  from typing import Iterable, Dict
label_desc: Use image node_id 2 for calling the MSELoss obj's underlying member method code with example usage: obj.forward(sentence_features, labels) and returns: self