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 on_backward(
      self,
      trainer: GradientDescentTrainer,
      batch_outputs: Dict[str, torch.Tensor],
      backward_called: bool,
      **kwargs,
  ) -> bool:
      if backward_called:
          raise OnBackwardException()
  
      if not hasattr(trainer.model, "predictor") or not hasattr(
          trainer.model, "adversary"
      ):
          raise ConfigurationError(
              "Model is expected to have `predictor` and `adversary` data members."
          )
  
      trainer.optimizer.zero_grad()
      # `retain_graph=True` prevents computation graph from being erased
      batch_outputs["adversary_loss"].backward(retain_graph=True)
      # trainer.model is expected to have `predictor` and `adversary` data members
      adversary_loss_grad = {
          name: param.grad.clone()
          for name, param in trainer.model.predictor.named_parameters()
          if param.grad is not None
      }
  
      trainer.model.predictor.zero_grad()
      batch_outputs["loss"].backward()
  
      with torch.no_grad():
          for name, param in trainer.model.predictor.named_parameters():
              if param.grad is not None:
                  unit_adversary_loss_grad = adversary_loss_grad[
                      name
                  ] / torch.linalg.norm(adversary_loss_grad[name])
                  # prevent predictor from accidentally aiding adversary
                  # by removing projection of predictor loss grad onto adversary loss grad
                  param.grad -= (
                      (param.grad * unit_adversary_loss_grad)
                      * unit_adversary_loss_grad
                  ).sum()
                  # make it difficult for adversary to recover protected variables
                  param.grad -= (
                      self.adversary_loss_weight
                      * adversary_loss_grad[name]
                  )
  
      # remove adversary_loss from computation graph
      batch_outputs["adversary_loss"] = batch_outputs[
          "adversary_loss"
      ].detach()
      return True
  
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 2 for calling the AdversarialBiasMitigatorBackwardCallback obj's underlying member method code with example usage: obj.on_backward(trainer, batch_outputs, backward_called) and returns: True