class_docstr: OnlineProcessor module definition.
  
  Args:
      feature_extractor: Feature extractor module.
      normalization_module: Normalization module.
      decoding_window: Size of the decoding window (in ms).
      encoder_sub_factor: Encoder subsampling factor.
      frontend_conf: Frontend configuration.
      device: Device to pin module tensors on.
      audio_sampling_rate: Input sampling rate.
docstr: Get features for current decoding window.
  
  Args:
      feats: Computed features sequence. (1, F, D_feats)
      feats_length: Computed features sequence length. (1,)
      is_final: Whether feats corresponds to the final chunk of data.
  
  Returns:
      feats: Decoding window features sequence. (1, chunk_sz_bs, D_feats)
      feats_length: Decoding window features length sequence. (1,)
docstr_tok: ["Get","features","for","current","decoding","window",".","Args",":","feats",":","Computed","features","sequence",".","(","1",",","F",",","D_feats",")","feats_length",":","Computed","features","sequence","length",".","(","1",",",")","is_final",":","Whether","feats","corresponds","to","the","final","chunk","of","data",".","Returns",":","feats",":","Decoding","window","features","sequence",".","(","1",",","chunk_sz_bs",",","D_feats",")","feats_length",":","Decoding","window","features","length","sequence",".","(","1",",",")"]
code: def get_current_feats(
      self,
      feats: torch.Tensor,
      feats_length: torch.Tensor,
      is_final: bool,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Get features for current decoding window.
  
      Args:
          feats: Computed features sequence. (1, F, D_feats)
          feats_length: Computed features sequence length. (1,)
          is_final: Whether feats corresponds to the final chunk of data.
  
      Returns:
          feats: Decoding window features sequence. (1, chunk_sz_bs, D_feats)
          feats_length: Decoding window features length sequence. (1,)
  
      """
      if self.feats is not None:
          if is_final:
              feats = feats.narrow(
                  1, self.trim_val, feats.size(1) - self.trim_val
              )
          else:
              feats = feats.narrow(
                  1, self.trim_val, feats.size(1) - 2 * self.trim_val
              )
  
          feats = torch.cat((self.feats, feats), dim=1)
      else:
          feats = feats.narrow(1, 0, feats.size(1) - self.trim_val)
  
      self.feats = feats[:, -self.offset_frames :, :]
  
      feats_length.fill_(feats.size(1))
  
      return feats, feats_length
  
imports: from typing import Dict, Tuple
  import torch
label_desc: Use image node_id 4 for calling the OnlineAudioProcessor obj's underlying member method code with example usage: obj.get_current_feats(feats, feats_length, is_final) and returns: feats,  feats_length