    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, ...).
        gold_labels : `torch.Tensor`, required.
            A tensor of the same shape as `predictions`.
        mask : `torch.BoolTensor`, optional (default = `None`).
            A tensor of the same shape as `predictions`.
        """
        predictions, gold_labels, mask = self.detach_tensors(predictions, gold_labels, mask)
        if not is_distributed():
            self._predictions_labels_covariance(predictions, gold_labels, mask)
            self._predictions_variance(predictions, predictions, mask)
            self._labels_variance(gold_labels, gold_labels, mask)
# Parameters

predictions : `torch.Tensor`, required.
A tensor of predictions of shape (batch_size, ...).
gold_labels : `torch.Tensor`, required.
A tensor of the same shape as `predictions`.
mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of the same shape as `predictions`.
allennlp/training/metrics/pearson_correlation.py