class_docstr: Binary loss for ASV Spoofing.
docstr: Prediction.
  Args:
      emb (torch.Tensor): encoder embedding output [Batch, T, enc_dim]
docstr_tok: ["Prediction",".","Args",":","emb","(","torch.Tensor",")",":","encoder","embedding","output","[","Batch",",","T",",","enc_dim","]"]
code: def score(self, emb: torch.Tensor):
      """Prediction.
      Args:
          emb (torch.Tensor): encoder embedding output [Batch, T, enc_dim]
      """
      emb = torch.mean(emb, dim=1)
      norms = torch.norm(emb, p=2, dim=-1, keepdim=True)
      nfeat = torch.div(emb, norms)
  
      norms_c = torch.norm(self.centers, p=2, dim=-1, keepdim=True)
      ncenters = torch.div(self.centers, norms_c)
      logits = torch.matmul(nfeat, torch.transpose(ncenters, 0, 1))
      return logits[:, 0]
  
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 3 for calling the ASVSpoofAMSoftmaxLoss obj's underlying member method code with example usage: obj.score(emb) and returns: logits