ece_loss/loss.py
def all_labels_as_unannotated(targets: Tensor) -> Tensor:
        r"""
        Input of targets must have shape as follows
        :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
        :param targets: matrix containing the class labels as integers
        :return: a mask indicating where the unannotated samples are
        """
        return torch.ones(len(targets))
r"""
        Input of targets must have shape as follows
        :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
        :param targets: matrix containing the class labels as integers
        :return: a mask indicating where the unannotated samples are
        
r"""
Input of targets must have shape as follows
r " " " Input of targets must have shape as follows
def all_labels_as_unannotated(targets: Tensor) -> Tensor:
        return torch.ones(len(targets))
def all_labels_as_unannotated ( targets : Tensor ) -> Tensor : return torch . ones ( len ( targets ) )
r"""
Input of targets must have shape as follows
r " " " Input of targets must have shape as follows
r"""
        Input of targets must have shape as follows
        :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
        :param targets: matrix containing the class labels as integers
        :return: a mask indicating where the unannotated samples are
        """
a mask indicating where the unannotated samples are
a mask indicating where the unannotated samples are
targets
Tensor
matrix containing the class labels as integers
matrix containing the class labels as integers
math
`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
` ( N ) ` where each value is : math : ` 0 \ leq \ text { targets } [ i ] \ leq C - 1 ` or