    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)}
# 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.
allennlp/fairness/adversarial_bias_mitigator.py