class_docstr: # Parameters
  
  bias_direction1 : `BiasDirectionWrapper`
      Bias direction of first concept subspace used by mitigator.
  bias_direction2 : `BiasDirectionWrapper`
      Bias direction of second concept subspace used by mitigator.
  embedding_layer : `torch.nn.Embedding`
      Embedding layer of base model.
  requires_grad : `bool`, optional (default=`True`)
      Option to enable gradient calculation for bias mitigator.
code: def train(self, mode: bool = True):
      self.mitigator.requires_grad = mode
      self.bias_direction1.train(mode)
      self.bias_direction2.train(mode)
  
imports: import torch
  from typing import Union, Optional
  from os import PathLike
  from allennlp.fairness.bias_mitigators import HardBiasMitigator, LinearBiasMitigator, INLPBiasMitigator, OSCaRBiasMitigator
  from allennlp.fairness.bias_direction_wrappers import BiasDirectionWrapper
  from allennlp.fairness.bias_utils import load_word_pairs
  from allennlp.common import Registrable
  from allennlp.data.tokenizers.tokenizer import Tokenizer
  from allennlp.data import Vocabulary
label_desc: Use image node_id 3 for calling the OSCaRBiasMitigatorWrapper obj's underlying member method code with example usage: obj.train(mode) without return types