fluence and examples.

    Legacy Example:
        >>> pred = torch.tensor([0.0, 1.0, 2.0, 3.0])
        >>> target = torch.tensor([0, 1, 1, 1])
        >>> fpr, tpr, thresholds = roc(pred, target, task='binary')
        >>> fpr
        tensor([0., 0., 0., 0., 1.])
        >>> tpr
        tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
        >>> thresholds
        tensor([1.0000, 0.9526, 0.8808, 0.7311, 0.5000])

        >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
        ...                      [0.05, 0.75, 0.05, 0.05],
        ...                      [0.05, 0.05, 0.75, 0.05],
        ...                      [0.05, 0.05, 0.05, 0.75]])
        >>> target = torch.tensor([0, 1, 3, 2])
        >>> fpr, tpr, thresholds = roc(pred, target, task='multiclass', num_classes=4)
        >>> fpr
        [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])]
        >>> tpr
        [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])]
        >>> thresholds
        [tensor([1.0000, 0.7500, 0.0500]),
         tensor([1.0000, 0.7500, 0.0500]),
         tensor([1.0000, 0.7500, 0.0500]),
         tensor([1.0000, 0.7500, 0.0500])]

        >>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
        ...                      [0.3584, 0.7576, 0.1183],
        ...                      [0.2286, 0.3468, 0.1338],
        ...                      [0.8603, 0.0745, 0.1837]])
        >>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]])
        >>> fpr, tpr, thresholds = roc(pred, target, task='multilabel', num_labels=3)
        >>> fpr
        [tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]),
         tensor([0., 0., 0., 1., 1.]),
         tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])]
        >>> tpr
        [tensor([0., 0., 1., 1., 1.]), tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]), tensor([0., 1., 1., 1., 1.])]
        >>> thresholds
        [tensor([1.0000, 0.8603, 0.8191, 0.3584, 0.2286]),
         tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]),
         tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])]

    z+`num_classes` is expected to be `int` but `z
 was passed.`z*`num_labels` is expected to be `int` but `z