code: def forward(self, input, num_mic):
      batch_size = input.size(0)
      nmic = input.size(1)
  
      # pad input accordingly
      input, rest = self.pad_input(input, self.window)
  
      # encoder on all channels
      enc_output = self.encoder(
          input.view(batch_size * nmic, 1, -1)
      )  # B*nmic, N, L
      seq_length = enc_output.shape[-1]
  
      # calculate the context of the encoder output
      # consider both past and future
      enc_context = self.signal_context(
          enc_output, self.context
      )  # B*nmic, N, 2C+1, L
      enc_context = enc_context.view(
          batch_size, nmic, self.enc_dim, -1, seq_length
      )  # B, nmic, N, 2C+1, L
  
      # NCC feature
      ref_enc = enc_context[:, 0].contiguous()  # B, N, 2C+1, L
      ref_enc = (
          ref_enc.permute(0, 3, 1, 2)
          .contiguous()
          .view(batch_size * seq_length, self.enc_dim, -1)
      )  # B*L, N, 2C+1
      enc_context_copy = (
          enc_context.permute(0, 4, 1, 3, 2)
          .contiguous()
          .view(batch_size * seq_length, nmic, -1, self.enc_dim)
      )  # B*L, nmic, 2C+1, N
      NCC = torch.cat(
          [
              enc_context_copy[:, i].bmm(ref_enc).unsqueeze(1)
              for i in range(nmic)
          ],
          1,
      )  # B*L, nmic, 2C+1, 2C+1
      ref_norm = (
          ref_enc.pow(2).sum(1).unsqueeze(1) + self.eps
      ).sqrt()  # B*L, 1, 2C+1
      enc_norm = (
          enc_context_copy.pow(2).sum(3).unsqueeze(3) + self.eps
      ).sqrt()  # B*L, nmic, 2C+1, 1
      NCC = NCC / (
          ref_norm.unsqueeze(1) * enc_norm
      )  # B*L, nmic, 2C+1, 2C+1
      NCC = torch.cat(
          [NCC[:, :, i] for i in range(NCC.shape[2])], 2
      )  # B*L, nmic, (2C+1)^2
      NCC = (
          NCC.view(batch_size, seq_length, nmic, -1)
          .permute(0, 2, 3, 1)
          .contiguous()
      )  # B, nmic, (2C+1)^2, L
  
      # context compression
      norm_output = self.enc_LN(enc_output)  # B*nmic, N, L
      norm_context = self.signal_context(
          norm_output, self.context
      )  # B*nmic, N, 2C+1, L
      norm_context = (
          norm_context.permute(0, 3, 2, 1)
          .contiguous()
          .view(-1, self.context * 2 + 1, self.enc_dim)
      )
      norm_context_BN = self.summ_BN(
          norm_context.view(-1, self.enc_dim)
      ).view(-1, self.context * 2 + 1, self.feature_dim)
      embedding = (
          self.summ_RNN(norm_context_BN)[0].transpose(1, 2).contiguous()
      )  # B*nmic*L, N, 2C+1
      embedding = norm_context_BN.transpose(
          1, 2
      ).contiguous() + self.summ_LN(
          embedding
      )  # B*nmic*L, N, 2C+1
      embedding = self.summ_output(embedding.mean(2)).view(
          batch_size, nmic, seq_length, self.enc_dim
      )  # B, nmic, L, N
      embedding = embedding.transpose(
          2, 3
      ).contiguous()  # B, nmic, N, L
  
      input_feature = torch.cat(
          [embedding, NCC], 2
      )  # B, nmic, N+(2C+1)^2, L
  
      # pass to DPRNN-TAC
      embedding = self.separator(input_feature, num_mic)[
          :, 0
      ].contiguous()  # B, nspk, N, L
  
      # concatenate with encoder outputs and generate masks
      # context decompression
      norm_context = norm_context.view(
          batch_size, nmic, seq_length, -1, self.enc_dim
      )  # B, nmic, L, 2C+1, N
      norm_context = norm_context.permute(0, 1, 4, 3, 2)[
          :, :1
      ].contiguous()  # B, 1, N, 2C+1, L
  
      embedding = torch.cat(
          [embedding.unsqueeze(3)] * (self.context * 2 + 1), 3
      )  # B, nspk, N, 2C+1, L
      norm_context = torch.cat(
          [norm_context] * self.num_spk, 1
      )  # B, nspk, N, 2C+1, L
      embedding = (
          torch.cat([norm_context, embedding], 2)
          .permute(0, 1, 4, 2, 3)
          .contiguous()
      )  # B, nspk, L, 2N, 2C+1
      all_filter = self.gen_BN(
          embedding.view(-1, self.enc_dim * 2, self.context * 2 + 1)
      )  # B*nspk*L, N, 2C+1
      all_filter = all_filter + self.gen_LN(
          self.gen_RNN(all_filter.transpose(1, 2))[0].transpose(1, 2)
      )  # B*nspk*L, N, 2C+1
      all_filter = self.gen_output(all_filter)  # B*nspk*L, N, 2C+1
      all_filter = all_filter.view(
          batch_size, self.num_spk, seq_length, self.enc_dim, -1
      )  # B, nspk, L, N+1, 2C+1
      all_filter = all_filter.permute(
          0, 1, 3, 4, 2
      ).contiguous()  # B, nspk, N, 2C+1, L
  
      # apply to with ref mic's encoder context
      output = (enc_context[:, :1] * all_filter).mean(
          3
      )  # B, nspk, N, L
  
      # decode
      bf_signal = self.decoder(
          output.view(batch_size * self.num_spk, self.enc_dim, -1)
      )  # B*nspk, 1, T
  
      if rest > 0:
          bf_signal = bf_signal[:, :, self.stride : -rest - self.stride]
  
      bf_signal = bf_signal.view(
          batch_size, self.num_spk, -1
      )  # B, nspk, T
  
      return bf_signal
  
file_path: espnet/espnet2/enh/layers/ifasnet.py
imports: import torch
  import torch.nn
  from espnet2.enh.layers import dprnn
  from espnet2.enh.layers.fasnet import BF_module, FaSNet_base
label_desc: Use image node_id 2 for calling the iFaSNet obj's underlying member method code with example usage: obj.forward(input, num_mic) and returns: bf_signal