class_docstr: Performs backpropagation for adversarial bias mitigation.
  While the adversary's gradients are computed normally,
  the predictor's gradients are computed such that updates to the
  predictor's parameters will not aid the adversary and will
  make it more difficult for the adversary to recover protected variables.
  
  !!! Note
      Intended to be used with `AdversarialBiasMitigator`.
      trainer.model is expected to have `predictor` and `adversary` data members.
  
  # Parameters
  
  adversary_loss_weight : `float`, optional (default = `1.0`)
      Quantifies how difficult predictor makes it for adversary to recover protected variables.
code: def __init__(
      self, serialization_dir: str, adversary_loss_weight: float = 1.0
  ) -> None:
      super().__init__(serialization_dir)
      self.adversary_loss_weight = adversary_loss_weight
  
imports: from typing import Dict, Optional
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.data import Vocabulary
  from allennlp.fairness.bias_direction_wrappers import BiasDirectionWrapper
  from allennlp.modules.feedforward import FeedForward
  from allennlp.models import Model
  from allennlp.nn import InitializerApplicator
  from allennlp.nn.util import find_embedding_layer
  from allennlp.training.callbacks.callback import TrainerCallback
  from allennlp.training.callbacks.backward import OnBackwardException
  from allennlp.training.gradient_descent_trainer import GradientDescentTrainer
label_desc: Use image node_id 1 to create a new AdversarialBiasMitigatorBackwardCallback object from inherited base classes: TrainerCallback with example: obj = AdversarialBiasMitigatorBackwardCallback(serialization_dir, adversary_loss_weight)