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.
docstr: :param model: SentenceTransformerModel
  :param similarity_fct:  Which similarity function to use.
docstr_tok: [":","param","model",":","SentenceTransformerModel",":","param","similarity_fct",":","Which","similarity","function","to","use","."]
code: def __init__(self, model, similarity_fct=util.pairwise_dot_score):
      """
      :param model: SentenceTransformerModel
      :param similarity_fct:  Which similarity function to use.
      """
      super(MarginMSELoss, self).__init__()
      self.model = model
      self.similarity_fct = similarity_fct
      self.loss_fct = nn.MSELoss()
  
imports: from ..None import util
  from torch import nn, Tensor
  from typing import Iterable, Dict
label_desc: Use image node_id 1 to create a new MarginMSELoss object from inherited base classes: nn with example: obj = MarginMSELoss(model, similarity_fct)