    def __call__(
        self,
        predictions: torch.Tensor,
        gold_labels: torch.Tensor,
        mask: Optional[torch.BoolTensor] = None,
    ):
        """
        # Parameters

        predictions : `torch.Tensor`, required.
            A tensor of predictions of shape (batch_size, k, sequence_length).
        gold_labels : `torch.Tensor`, required.
            A tensor of integer class label of shape (batch_size, sequence_length).
        mask : `torch.BoolTensor`, optional (default = `None`).
            A masking tensor the same size as `gold_labels`.
        """
        predictions, gold_labels, mask = self.detach_tensors(predictions, gold_labels, mask)

        # Some sanity checks.
        if gold_labels.dim() != predictions.dim() - 1:
            raise ConfigurationError(
                "gold_labels must have dimension == predictions.dim() - 1 but "
                "found tensor of shape: {}".format(gold_labels.size())
            )
        if mask is not None and mask.size() != gold_labels.size():
            raise ConfigurationError(
                "mask must have the same size as predictions but "
                "found tensor of shape: {}".format(mask.size())
            )

        k = predictions.size()[1]
        expanded_size = list(gold_labels.size())
        expanded_size.insert(1, k)
        expanded_gold = gold_labels.unsqueeze(1).expand(expanded_size)

        if mask is not None:
            expanded_mask = mask.unsqueeze(1).expand(expanded_size)
            masked_gold = expanded_mask * expanded_gold
            masked_predictions = expanded_mask * predictions
        else:
            masked_gold = expanded_gold
            masked_predictions = predictions

        eqs = masked_gold.eq(masked_predictions)
        matches_per_question = eqs.min(dim=2)[0]
        some_match = matches_per_question.max(dim=1)[0]
        correct = some_match.sum().item()

        _total_count = predictions.size()[0]
        _correct_count = correct

        self.correct_count += dist_reduce_sum(_correct_count)
        self.total_count += dist_reduce_sum(_total_count)
# Parameters

predictions : `torch.Tensor`, required.
A tensor of predictions of shape (batch_size, k, sequence_length).
gold_labels : `torch.Tensor`, required.
A tensor of integer class label of shape (batch_size, sequence_length).
mask : `torch.BoolTensor`, optional (default = `None`).
A masking tensor the same size as `gold_labels`.
allennlp/training/metrics/sequence_accuracy.py