code: def __init__(
      self,
      input_dim,
      feature_dim,
      hidden_dim,
      output_dim,
      num_spk=2,
      layer=4,
      segment_size=100,
      bidirectional=True,
      dropout=0.0,
      fasnet_type="ifasnet",
  ):
      super().__init__()
  
      assert fasnet_type in [
          "fasnet",
          "ifasnet",
      ], "fasnet_type should be fasnet or ifasnet"
  
      self.input_dim = input_dim
      self.feature_dim = feature_dim
      self.hidden_dim = hidden_dim
      self.output_dim = output_dim
  
      self.layer = layer
      self.segment_size = segment_size
      self.num_spk = num_spk
  
      self.dprnn_model = dprnn.DPRNN_TAC(
          "lstm",
          self.feature_dim,
          self.hidden_dim,
          self.feature_dim * self.num_spk,
          num_layers=layer,
          bidirectional=bidirectional,
          dropout=dropout,
      )
      self.eps = 1e-8
  
      self.fasnet_type = fasnet_type
  
      if fasnet_type == "ifasnet":
          # output layer in ifasnet
          self.output = nn.Conv1d(self.feature_dim, self.output_dim, 1)
      elif fasnet_type == "fasnet":
          # gated output layer in ifasnet
          self.output = nn.Sequential(
              nn.Conv1d(self.feature_dim, self.output_dim, 1), nn.Tanh()
          )
          self.output_gate = nn.Sequential(
              nn.Conv1d(self.feature_dim, self.output_dim, 1),
              nn.Sigmoid(),
          )
  
      self.num_spk = num_spk
      self.BN = nn.Conv1d(
          self.input_dim, self.feature_dim, 1, bias=False
      )
  
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 BF_module object from inherited base classes: nn with example: obj = BF_module(input_dim, feature_dim, hidden_dim, output_dim, num_spk, layer, segment_size, bidirectional, dropout, fasnet_type)