code: def split_feature(self, x):
      B, N, T = x.size()
      unfolded = torch.nn.functional.unfold(
          x.unsqueeze(-1),
          kernel_size=(self.segment_size, 1),
          padding=(self.segment_size, 0),
          stride=(self.segment_size // 2, 1),
      )
      return unfolded.reshape(B, N, self.segment_size, -1)
  
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 3 for calling the DPTNetSeparator obj's underlying member method code with example usage: obj.split_feature(x) and returns: unfolded