code: def forward(self, input, num_mic):
      # input: (B, ch, N, T)
      batch_size, ch, N, seq_length = input.shape
  
      input = input.view(batch_size * ch, N, seq_length)  # B*ch, N, T
      enc_feature = self.BN(input)
  
      # split the encoder output into overlapped, longer segments
      enc_segments, enc_rest = dprnn.split_feature(
          enc_feature, self.segment_size
      )  # B*ch, N, L, K
  
      enc_segments = enc_segments.view(
          batch_size,
          ch,
          -1,
          enc_segments.shape[2],
          enc_segments.shape[3],
      )  # B, ch, N, L, K
      output = self.dprnn_model(enc_segments, num_mic).view(
          batch_size * ch * self.num_spk,
          self.feature_dim,
          self.segment_size,
          -1,
      )  # B*ch*nspk, N, L, K
      # overlap-and-add of the outputs
      output = dprnn.merge_feature(output, enc_rest)  # B*ch*nspk, N, T
  
      if self.fasnet_type == "fasnet":
          # gated output layer for filter generation
          bf_filter = self.output(output) * self.output_gate(
              output
          )  # B*ch*nspk, K, T
          bf_filter = (
              bf_filter.transpose(1, 2)
              .contiguous()
              .view(batch_size, ch, self.num_spk, -1, self.output_dim)
          )  # B, ch, nspk, L, N
  
      elif self.fasnet_type == "ifasnet":
          # output layer
          bf_filter = self.output(output)  # B*ch*nspk, K, T
          bf_filter = bf_filter.view(
              batch_size, ch, self.num_spk, self.output_dim, -1
          )  # B, ch, nspk, K, L
  
      return bf_filter
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 2 for calling the BF_module obj's underlying member method code with example usage: obj.forward(input, num_mic) and returns: bf_filter