code: def convert_audio_channels(
      wav: torch.Tensor, channels: int = 2
  ) -> torch.Tensor:
      """Convert audio to the given number of channels.
  
      Args:
          wav (torch.Tensor): Audio wave of shape [B, C, T].
          channels (int): Expected number of channels as output.
      Returns:
          torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
      """
      *shape, src_channels, length = wav.shape
      if src_channels == channels:
          pass
      elif channels == 1:
          # Case 1:
          # The caller asked 1-channel audio, and the stream has multiple
          # channels, downmix all channels.
          wav = wav.mean(dim=-2, keepdim=True)
      elif src_channels == 1:
          # Case 2:
          # The caller asked for multiple channels, but the input file has
          # a single channel, replicate the audio over all channels.
          wav = wav.expand(*shape, channels, length)
      elif src_channels >= channels:
          # Case 3:
          # The caller asked for multiple channels, and the input file has
          # more channels than requested. In that case return the first channels.
          wav = wav[..., :channels, :]
      else:
          # Case 4: What is a reasonable choice here?
          raise ValueError(
              "The audio file has less channels than requested but is not mono."
          )
      return wav
  
file_path: audiocraft/audiocraft/data/audio_utils.py
imports: import sys
  import typing
  import julius
  import torch
  import torchaudio
label_desc: Use image node_id 1 for calling a global function with example usage: convert_audio_channels(wav, channels) and returns: wav