code: def forward(self, input, num_mic):
      batch_size = input.size(0)
      nmic = input.size(1)
  
      # split input into chunks
      all_seg, all_mic_context, rest = self.seg_signal_context(
          input, self.window, self.context
      )  # B, nmic, L, win/chunk
      seq_length = all_seg.size(2)
  
      # embeddings for all channels
      enc_output = (
          self.encoder(
              all_mic_context.view(
                  -1, 1, self.context * 2 + self.window
              )
          )
          .view(batch_size * nmic, seq_length, self.enc_dim)
          .transpose(1, 2)
          .contiguous()
      )  # B*nmic, N, L
      enc_output = self.enc_LN(enc_output).view(
          batch_size, nmic, self.enc_dim, seq_length
      )  # B, nmic, N, L
  
      # calculate the cosine similarities for ref channel's center
      # frame with all channels' context
  
      ref_seg = (
          all_seg[:, 0].contiguous().view(1, -1, self.window)
      )  # 1, B*L, win
      all_context = (
          all_mic_context.transpose(0, 1)
          .contiguous()
          .view(nmic, -1, self.context * 2 + self.window)
      )  # 1, B*L, 3*win
      all_cos_sim = self.seq_cos_sim(
          all_context, ref_seg
      )  # nmic, B*L, 2*win+1
      all_cos_sim = (
          all_cos_sim.view(
              nmic, batch_size, seq_length, self.filter_dim
          )
          .permute(1, 0, 3, 2)
          .contiguous()
      )  # B, nmic, 2*win+1, L
  
      input_feature = torch.cat(
          [enc_output, all_cos_sim], 2
      )  # B, nmic, N+2*win+1, L
  
      # pass to DPRNN
      all_filter = self.all_BF(
          input_feature, num_mic
      )  # B, ch, nspk, L, 2*win+1
  
      # convolve with all mic's context
      mic_context = torch.cat(
          [
              all_mic_context.view(
                  batch_size * nmic,
                  1,
                  seq_length,
                  self.context * 2 + self.window,
              )
          ]
          * self.num_spk,
          1,
      )  # B*nmic, nspk, L, 3*win
      all_bf_output = F.conv1d(
          mic_context.view(1, -1, self.context * 2 + self.window),
          all_filter.view(-1, 1, self.filter_dim),
          groups=batch_size * nmic * self.num_spk * seq_length,
      )  # 1, B*nmic*nspk*L, win
      all_bf_output = all_bf_output.view(
          batch_size, nmic, self.num_spk, seq_length, self.window
      )  # B, nmic, nspk, L, win
  
      # reshape to utterance
      bf_signal = all_bf_output.view(
          batch_size * nmic * self.num_spk, -1, self.window * 2
      )
      bf_signal1 = (
          bf_signal[:, :, : self.window]
          .contiguous()
          .view(batch_size * nmic * self.num_spk, 1, -1)[
              :, :, self.stride :
          ]
      )
      bf_signal2 = (
          bf_signal[:, :, self.window :]
          .contiguous()
          .view(batch_size * nmic * self.num_spk, 1, -1)[
              :, :, : -self.stride
          ]
      )
      bf_signal = bf_signal1 + bf_signal2  # B*nmic*nspk, 1, T
      if rest > 0:
          bf_signal = bf_signal[:, :, :-rest]
  
      bf_signal = bf_signal.view(
          batch_size, nmic, self.num_spk, -1
      )  # B, nmic, nspk, T
      # consider only the valid channels
      if num_mic.max() == 0:
          bf_signal = bf_signal.mean(1)  # B, nspk, T
      else:
          bf_signal = [
              bf_signal[b, : num_mic[b]].mean(0).unsqueeze(0)
              for b in range(batch_size)
          ]  # nspk, T
          bf_signal = torch.cat(bf_signal, 0)  # B, nspk, T
  
      return bf_signal
  
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 FaSNet_TAC obj's underlying member method code with example usage: obj.forward(input, num_mic) and returns: bf_signal