    def __call__(self, nli_probabilities: torch.Tensor) -> None:  # type: ignore
        """

        # Parameters

        !!! Note
            In the examples below, we treat gender identity as binary, which does not accurately
            characterize gender in real life.

        nli_probabilities : `torch.Tensor`, required.
            A tensor of size (batch_size, ..., 3) containing natural language inference
            (i.e. entailment, contradiction, and neutral) probabilities for neutrally-constructed
            pairs of sentences differing only in the subject. For example, if the concept is gender,
            nli_probabilities could contain the natural language inference probabilities of:

            - "The driver owns a cabinet." -> "The man owns a cabinet."

            - "The driver owns a cabinet." -> "The woman owns a cabinet."

            - "The doctor eats an apple." -> "The man eats an apple."

            - "The doctor eats an apple." -> "The woman eats an apple."
        """
        nli_probabilities = nli_probabilities.detach()

        # Some sanity checks
        if nli_probabilities.dim() < 2:
            raise ConfigurationError(
                "nli_probabilities must have at least two dimensions but "
                "found tensor of shape: {}".format(nli_probabilities.size())
            )
        if nli_probabilities.size(-1) != 3:
            raise ConfigurationError(
                "Last dimension of nli_probabilities must have dimensionality of 3 but "
                "found tensor of shape: {}".format(nli_probabilities.size())
            )

        _nli_neutral_probs = nli_probabilities[..., self.neutral_label]

        self._nli_probs_sum += dist_reduce_sum(_nli_neutral_probs.sum().item())
        self._num_neutral_predictions += dist_reduce_sum(
            (nli_probabilities.argmax(dim=-1) == self.neutral_label).float().sum().item()
        )
        for tau in self.taus:
            self._num_neutral_above_taus[tau] += dist_reduce_sum(
                (_nli_neutral_probs > tau).float().sum().item()
            )
        self._total_predictions += dist_reduce_sum(_nli_neutral_probs.numel())
# Parameters

!!! Note
In the examples below, we treat gender identity as binary, which does not accurately
characterize gender in real life.

nli_probabilities : `torch.Tensor`, required.
A tensor of size (batch_size, ..., 3) containing natural language inference
(i.e. entailment, contradiction, and neutral) probabilities for neutrally-constructed
pairs of sentences differing only in the subject. For example, if the concept is gender,
nli_probabilities could contain the natural language inference probabilities of:

- "The driver owns a cabinet." -> "The man owns a cabinet."

- "The driver owns a cabinet." -> "The woman owns a cabinet."

- "The doctor eats an apple." -> "The man eats an apple."

- "The doctor eats an apple." -> "The woman eats an apple."
allennlp/fairness/bias_metrics.py