class_docstr: Binary loss for ASV Spoofing.
docstr: Forward.
  Args:
      label (torch.Tensor): ground truth label [Batch, 1]
      emb   (torch.Tensor): encoder embedding output [Batch, T, enc_dim]
docstr_tok: ["Forward",".","Args",":","label","(","torch.Tensor",")",":","ground","truth","label","[","Batch",",","1","]","emb","(","torch.Tensor",")",":","encoder","embedding","output","[","Batch",",","T",",","enc_dim","]"]
code: def forward(self, label: torch.Tensor, emb: torch.Tensor, **kwargs):
      """Forward.
      Args:
          label (torch.Tensor): ground truth label [Batch, 1]
          emb   (torch.Tensor): encoder embedding output [Batch, T, enc_dim]
      """
      emb = torch.mean(emb, dim=1)
      w = torch.nn.functional.normalize(self.center, p=2, dim=1)
      x = torch.nn.functional.normalize(emb, p=2, dim=1)
  
      # TODO1 (exercise 2): compute scores based on w and x
  
      # TODO2 (exercise 2): calculate the score bias based on m_real and m_fake
  
      # TODO3 (exercise 2): apply alpha and softplus
  
      # TODO4 (exercise 2): returnthe final loss
      return None
  
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 ASVSpoofOCSoftmaxLoss obj's underlying member method code with example usage: obj.forward(label, emb) and returns: None