fixbug: use BinaryPrecisionRecallCurve instead of PrecisionRecallCurve

[rglkt]

📝 Description

when I try to use aupr in my program, some error happen as follow:

  File "xxx/envs/anomalib_env/lib/python3.10/site-packages/anomalib/metrics/__init__.py", line 60, in metric_collection_from_names
    metrics.add_metrics(metric_cls())
TypeError: PrecisionRecallCurve.__new__() missing 1 required positional argument: 'task'

I found that's because PrecisionRecallCurve required positional argument: 'task'. We can use use BinaryPrecisionRecallCurve instead of PrecisionRecallCurve to solve this problem

PrecisionRecallCurve
    This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
    :class:`~torchmetrics.classification.BinaryPrecisionRecallCurve`,
    :class:`~torchmetrics.classification.MulticlassPrecisionRecallCurve` and
    :class:`~torchmetrics.classification.MultilabelPrecisionRecallCurve` for the specific details of each argument
    influence and examples.

    Legacy Example:
        >>> pred = torch.tensor([0, 0.1, 0.8, 0.4])
        >>> target = torch.tensor([0, 1, 1, 0])
        >>> pr_curve = PrecisionRecallCurve(task="binary")

• 🛠️ Fixes # (issue number)

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[samet-akcay]

thanks for the fix!