class_docstr: Transposed Convolutional decoder for speech enhancement and separation
docstr: streaming_merge. It merges the frame-level processed audio chunks
  in the streaming *simulation*. It is noted that, in real applications,
  the processed audio should be sent to the output channel frame by frame.
  You may refer to this function to manage your streaming output buffer.
  
  Args:
      chunks: List [(B, frame_size),]
      ilens: [B]
  Returns:
      merge_audio: [B, T]
code: def streaming_merge(
      self, chunks: torch.Tensor, ilens: torch.tensor = None
  ):
      """streaming_merge. It merges the frame-level processed audio chunks
      in the streaming *simulation*. It is noted that, in real applications,
      the processed audio should be sent to the output channel frame by frame.
      You may refer to this function to manage your streaming output buffer.
  
      Args:
          chunks: List [(B, frame_size),]
          ilens: [B]
      Returns:
          merge_audio: [B, T]
      """
      hop_size = self.stride
      frame_size = self.kernel_size
  
      num_chunks = len(chunks)
      batch_size = chunks[0].shape[0]
      audio_len = (
          int(hop_size * num_chunks + frame_size - hop_size)
          if not ilens
          else ilens.max()
      )
  
      output = torch.zeros(
          (batch_size, audio_len), dtype=chunks[0].dtype
      ).to(chunks[0].device)
  
      for i, chunk in enumerate(chunks):
          output[:, i * hop_size : i * hop_size + frame_size] += chunk
  
      return output
  
file_path: espnet/espnet2/enh/decoder/conv_decoder.py
imports: import math
  import torch
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
label_desc: Use image node_id 4 for calling the ConvDecoder obj's underlying member method code with example usage: obj.streaming_merge(chunks, ilens) and returns: output