class_docstr: Binary loss for ASV Spoofing.
docstr: Forward.
  Args:
      pred  (torch.Tensor): prediction probability [Batch, 2]
      label (torch.Tensor): ground truth label [Batch, 2]
docstr_tok: ["Forward",".","Args",":","pred","(","torch.Tensor",")",":","prediction","probability","[","Batch",",","2","]","label","(","torch.Tensor",")",":","ground","truth","label","[","Batch",",","2","]"]
code: def forward(self, pred: torch.Tensor, label: torch.Tensor, **kwargs):
      """Forward.
      Args:
          pred  (torch.Tensor): prediction probability [Batch, 2]
          label (torch.Tensor): ground truth label [Batch, 2]
      """
      loss = self.loss(
          self.sigmoid(pred.view(-1)), label.view(-1).float()
      )
      return loss
  
imports: import torch
  from espnet2.asvspoof.loss.abs_loss import AbsASVSpoofLoss
  from espnet.nets.pytorch_backend.nets_utils import to_device
label_desc: Use image node_id 2 for calling the ASVSpoofBinaryLoss obj's underlying member method code with example usage: obj.forward(pred, label) and returns: loss