docstr: time-frequency L1 loss.
  
  Args:
      ref: (Batch, T, F) or (Batch, T, C, F)
      inf: (Batch, T, F) or (Batch, T, C, F)
  Returns:
      loss: (Batch,)
docstr_tok: ["time-frequency","L1","loss",".","Args",":","ref",":","(","Batch",",","T",",","F",")","or","(","Batch",",","T",",","C",",","F",")","inf",":","(","Batch",",","T",",","F",")","or","(","Batch",",","T",",","C",",","F",")","Returns",":","loss",":","(","Batch",",",")"]
code: def forward(self, ref, inf) -> torch.Tensor:
      """time-frequency L1 loss.
  
      Args:
          ref: (Batch, T, F) or (Batch, T, C, F)
          inf: (Batch, T, F) or (Batch, T, C, F)
      Returns:
          loss: (Batch,)
      """
      assert ref.shape == inf.shape, (ref.shape, inf.shape)
  
      if is_complex(inf):
          l1loss = (
              abs(ref.real - inf.real)
              + abs(ref.imag - inf.imag)
              + abs(ref.abs() - inf.abs())
          )
      else:
          l1loss = abs(ref - inf)
      if ref.dim() == 3:
          l1loss = l1loss.mean(dim=[1, 2])
      elif ref.dim() == 4:
          l1loss = l1loss.mean(dim=[1, 2, 3])
      else:
          raise ValueError(
              "Invalid input shape: ref={}, inf={}".format(
                  ref.shape, inf.shape
              )
          )
      return l1loss
  
imports: import math
  from abc import ABC, abstractmethod
  from functools import reduce
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from espnet2.enh.layers.complex_utils import complex_norm, is_complex, new_complex_like
  from espnet2.enh.loss.criterions.abs_loss import AbsEnhLoss
label_desc: Use image node_id 4 for calling the FrequencyDomainL1 obj's underlying member method code with example usage: obj.forward(ref, inf) and returns: l1loss