docstr: Called as forward hook.
code: def __call__(self, module, module_in, module_out):
      """
      Called as forward hook.
      """
      with torch.no_grad():
          # mean pooling over static word embeddings to get sentence embedding
          module_out = module_out.mean(dim=1)
          self.adversary_label = torch.matmul(
              module_out,
              self.predetermined_bias_direction.to(module_out.device),
          ).unsqueeze(-1)
  
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 _AdversaryLabelHook obj's underlying member method code with example usage: obj.__call__(module, module_in, module_out) without return types