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 __init__(
      self,
      bias_direction1: BiasDirectionWrapper,
      bias_direction2: BiasDirectionWrapper,
      embedding_layer: torch.nn.Embedding,
      requires_grad: bool = True,
  ):
      # use predetermined bias directions
      self.bias_direction1 = bias_direction1
      self.predetermined_bias_direction1 = self.bias_direction1(
          embedding_layer
      )
      self.bias_direction2 = bias_direction2(embedding_layer)
      self.predetermined_bias_direction2 = self.bias_direction2(
          embedding_layer
      )
      self.mitigator = OSCaRBiasMitigator(requires_grad=requires_grad)
  
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 1 to create a new OSCaRBiasMitigatorWrapper object from inherited base classes: BiasMitigatorWrapper with example: obj = OSCaRBiasMitigatorWrapper(bias_direction1, bias_direction2, embedding_layer, requires_grad)