Add SpecificityAtSensitivity Metric

[shenoynikhil]

What does this PR do?

Partially Fixes #971. Introduces the SpecificityAtSensitivity Metric. The computation is as follows,

1. Compute false positive rate, true positive rate at different thresholds (Using the roc curve)
2. true positive rate is sensitivity while 1 - false positive rate is specificity
3. Consider all thresholds where sensitivity >= min_sensitivity (this is provided by the user)
4. Calculate the maximum possible specificity at all the thresholds

PseudoCode

from torchmetrics.functional.classification.roc import _binary_roc_compute

preds, target, thresholds = _binary_precision_recall_curve_format(preds, target, thresholds, ignore_index)
state = _binary_precision_recall_curve_update(preds, target, thresholds)
fpr, sensitivity, thresholds = _binary_roc_compute(state, thresholds, pos_label)
specificity = 1 - fpr

# actual computation of max specificity at min possible sensitivity
max_spec, _, best_threshold = max(
    [(sp, sn, thresh) for sp, sn, thresh in zip(specificity, sensitivity, thresholds) if sn >= min_sensitivity]
)

return max_spec, best_threshold

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• Did you write any new necessary tests?

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[shenoynikhil]

@Borda @stancld @SkafteNicki If you can provide some feedback on the edge cases (all positive and all negative), that would be great. I just wrote the edge case based on the recall_at_fixed_precision metric.

[codecov]

Codecov Report

Merging #1432 (d9e1fa3) into master (aab3a3b) will decrease coverage by 38%.
The diff coverage is 86%.

Additional details and impacted files

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- Hits         9717    5657   -4060     
- Misses       1122    5329   +4207