class_docstr: STFT encoder for speech enhancement and separation
docstr: Forward.
  
  Args:
      input (torch.Tensor): mixed speech [Batch, frame_length]
  Return:
      B, 1, F
docstr_tok: ["Forward",".","Args",":","input","(","torch.Tensor",")",":","mixed","speech","[","Batch",",","frame_length","]","Return",":","B",",","1",",","F"]
code: def forward_streaming(self, input: torch.Tensor):
      """Forward.
  
      Args:
          input (torch.Tensor): mixed speech [Batch, frame_length]
      Return:
          B, 1, F
      """
  
      assert (
          input.dim() == 2
      ), "forward_streaming only support for single-channel input currently."
  
      windowed = self._apply_window_func(input)
  
      feature = (
          torch.fft.rfft(windowed)
          if self.stft.onesided
          else torch.fft.fft(windowed)
      )
      feature = feature.unsqueeze(1)
      if not (is_torch_1_9_plus and self.use_builtin_complex):
          feature = ComplexTensor(feature.real, feature.imag)
  
      feature = self.spec_transform_func(feature)
  
      return feature
  
file_path: espnet/espnet2/enh/encoder/stft_encoder.py
imports: import torch
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.layers.stft import Stft
label_desc: Use image node_id 8 for calling the STFTEncoder obj's underlying member method code with example usage: obj.forward_streaming(input) and returns: feature