class_docstr: Compute the MSE loss between the ``|sim(Query, Pos) - sim(Query, Neg)|`` and ``|gold_sim(Query, Pos) - gold_sim(Query, Neg)|``.
  By default, sim() is the dot-product.
  For more details, please refer to https://arxiv.org/abs/2010.02666.
code: def forward(
      self,
      sentence_features: Iterable[Dict[str, Tensor]],
      labels: Tensor,
  ):
      # sentence_features: query, positive passage, negative passage
      reps = [
          self.model(sentence_feature)["sentence_embedding"]
          for sentence_feature in sentence_features
      ]
      embeddings_query = reps[0]
      embeddings_pos = reps[1]
      embeddings_neg = reps[2]
  
      scores_pos = self.similarity_fct(embeddings_query, embeddings_pos)
      scores_neg = self.similarity_fct(embeddings_query, embeddings_neg)
      margin_pred = scores_pos - scores_neg
  
      return self.loss_fct(margin_pred, labels)
  
imports: from ..None import util
  from torch import nn, Tensor
  from typing import Iterable, Dict
label_desc: Use image node_id 2 for calling the MarginMSELoss obj's underlying member method code with example usage: obj.forward(sentence_features, labels) and returns: self