code: def __init__(self, *args, **kwargs):
      super(FaSNet_TAC, self).__init__(*args, **kwargs)
  
      self.context = int(self.sr * self.context_len / 1000)
      self.filter_dim = self.context * 2 + 1
  
      # DPRNN + TAC for estimation
      self.all_BF = BF_module(
          self.filter_dim + self.enc_dim,
          self.feature_dim,
          self.hidden_dim,
          self.filter_dim,
          self.num_spk,
          self.layer,
          self.segment_size,
          dropout=self.dropout,
          fasnet_type="fasnet",
      )
  
      # waveform encoder
      self.encoder = nn.Conv1d(
          1, self.enc_dim, self.context * 2 + self.window, bias=False
      )
      self.enc_LN = nn.GroupNorm(1, self.enc_dim, eps=1e-8)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 1 to create a new FaSNet_TAC object from inherited base classes: FaSNet_base with example: obj = FaSNet_TAC()