code: def forward_streaming(self, input_frame: torch.Tensor, buffer=None):
      # input_frame: B, 1, N
  
      B, _, N = input_frame.shape
  
      receptive_field = self.tcn.receptive_field
  
      if buffer is None:
          buffer = torch.zeros(
              (B, receptive_field, N), device=input_frame.device
          )
  
      buffer = torch.roll(buffer, shifts=-1, dims=1)
      buffer[:, -1, :] = input_frame[:, 0, :]
  
      masked, ilens, others = self.forward(buffer, None)
  
      masked = [m[:, -1, :].unsqueeze(1) for m in masked]
  
      return masked, buffer, others
  
file_path: espnet/espnet2/enh/separator/tcn_separator.py
imports: from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import is_complex
  from espnet2.enh.layers.tcn import TemporalConvNet
  from espnet2.enh.separator.abs_separator import AbsSeparator
label_desc: Use image node_id 3 for calling the TCNSeparator obj's underlying member method code with example usage: obj.forward_streaming(input_frame, buffer) and returns: masked,  buffer,  others