code: def merge_feature(self, x, length=None):
      B, N, L, n_chunks = x.size()
      hop_size = self.segment_size // 2
      if length is None:
          length = (n_chunks - 1) * hop_size + L
          padding = 0
      else:
          padding = (0, L)
  
      seq = x.reshape(B, N * L, n_chunks)
      x = torch.nn.functional.fold(
          seq,
          output_size=(1, length),
          kernel_size=(1, L),
          padding=padding,
          stride=(1, hop_size),
      )
      norm_mat = torch.nn.functional.fold(
          input=torch.ones_like(seq),
          output_size=(1, length),
          kernel_size=(1, L),
          padding=padding,
          stride=(1, hop_size),
      )
  
      x /= norm_mat
  
      return x.reshape(B, N, length)
  
file_path: espnet/espnet2/enh/separator/dptnet_separator.py
imports: from collections import OrderedDict
  from distutils.version import LooseVersion
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import is_complex
  from espnet2.enh.layers.dptnet import DPTNet
  from espnet2.enh.layers.tcn import choose_norm
  from espnet2.enh.separator.abs_separator import AbsSeparator
label_desc: Use image node_id 4 for calling the DPTNetSeparator obj's underlying member method code with example usage: obj.merge_feature(x, length) and returns: x