class_docstr: Mixin class for applying the R3F method, to apply R3F with any model
  inherit the class and implement the abstract functions.
  
  For more details: https://arxiv.org/abs/2008.03156
class_docstr_tok: ["Mixin","class","for","applying","the","R3F","method",",","to","apply","R3F","with","any","model","inherit","the","class","and","implement","the","abstract","functions",".","For","more","details",":","https",":","\/\/arxiv.org\/abs\/2008.03156"]
docstr: Given the core model outputs, this returns the embedding module that is used
  for the R3F loss, in particular noise will be injected to this module.
docstr_tok: ["Given","the","core","model","outputs",",","this","returns","the","embedding","module","that","is","used","for","the","R3F","loss",",","in","particular","noise","will","be","injected","to","this","module","."]
code: def get_embedding_module(self, *args, **kwargs):
      """
      Given the core model outputs, this returns the embedding module that is used
      for the R3F loss, in particular noise will be injected to this module.
      """
      raise NotImplementedError()
  
imports: from contextlib import AbstractContextManager
  from enum import Enum
  from typing import Dict
  import torch
  import torch.nn.functional
  from pytext.common.constants import Stage
  from pytext.config import ConfigBase
  from pytext.utils.precision import maybe_float
label_desc: Use image node_id 2 for calling the R3FPyTextMixin obj's underlying member method code with example usage: obj.get_embedding_module() without return types