class_docstr: This `Model` implements a simple feedforward regression adversary.
  
  Registered as a `Model` with name "feedforward_regression_adversary".
  
  # Parameters
  
  vocab : `Vocabulary`
  feedforward : `FeedForward`
      A feedforward layer.
  initializer : `Optional[InitializerApplicator]`, optional (default=`InitializerApplicator()`)
      If provided, will be used to initialize the model parameters.
docstr: # Parameters
  
  input : `torch.FloatTensor`
      A tensor of size (batch_size, ...).
  label : `torch.FloatTensor`
      A tensor of the same size as input.
  
  # Returns
  
  An output dictionary consisting of:
      - `loss` : `torch.FloatTensor`
          A scalar loss to be optimised.
code: def forward(  # type: ignore
      self, input: torch.FloatTensor, label: torch.FloatTensor
  ) -> Dict[str, torch.Tensor]:
      """
      # Parameters
  
      input : `torch.FloatTensor`
          A tensor of size (batch_size, ...).
      label : `torch.FloatTensor`
          A tensor of the same size as input.
  
      # Returns
  
      An output dictionary consisting of:
          - `loss` : `torch.FloatTensor`
              A scalar loss to be optimised.
      """
  
      pred = self._feedforward(input)
      return {"loss": self._loss(pred, label)}
  
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 FeedForwardRegressionAdversary obj's underlying member method code with example usage: obj.forward(input, label) and returns: dict