code: def forward(
      self, ref: torch.Tensor, inf: torch.Tensor
  ) -> torch.Tensor:
      # the return tensor should be shape of (batch,)
  
      noise = inf - ref
  
      snr = 20 * (
          torch.log10(torch.norm(ref, p=2, dim=1).clamp(min=self.eps))
          - torch.log10(
              torch.norm(noise, p=2, dim=1).clamp(min=self.eps)
          )
      )
      return -snr
  
imports: import logging
  import math
  from abc import ABC
  import ci_sdr
  import fast_bss_eval
  import torch
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.loss.criterions.abs_loss import AbsEnhLoss
  from espnet2.layers.stft import Stft
label_desc: Use image node_id 2 for calling the SNRLoss obj's underlying member method code with example usage: obj.forward(ref, inf) and returns: unknown