code: def apply_codec(
      waveform: Tensor,
      sample_rate: int,
      format: str,
      channels_first: bool = True,
      compression: Optional[float] = None,
      encoding: Optional[str] = None,
      bits_per_sample: Optional[int] = None,
  ) -> Tensor:
      r"""
      Apply codecs as a form of augmentation.
  
      .. devices:: CPU
  
      Args:
          waveform (Tensor): Audio data. Must be 2 dimensional. See also ```channels_first```.
          sample_rate (int): Sample rate of the audio waveform.
          format (str): File format.
          channels_first (bool, optional):
              When True, both the input and output Tensor have dimension `(channel, time)`.
              Otherwise, they have dimension `(time, channel)`.
          compression (float or None, optional): Used for formats other than WAV.
              For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
          encoding (str or None, optional): Changes the encoding for the supported formats.
              For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
          bits_per_sample (int or None, optional): Changes the bit depth for the supported formats.
              For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
  
      Returns:
          Tensor: Resulting Tensor.
          If ``channels_first=True``, it has `(channel, time)` else `(time, channel)`.
      """
      from torchaudio.backend import _sox_io_backend
  
      with tempfile.NamedTemporaryFile() as f:
          torchaudio.backend._sox_io_backend.save(
              f.name,
              waveform,
              sample_rate,
              channels_first,
              compression,
              format,
              encoding,
              bits_per_sample,
          )
          augmented, sr = _sox_io_backend.load(
              f.name, channels_first=channels_first, format=format
          )
      if sr != sample_rate:
          augmented = resample(augmented, sr, sample_rate)
      return augmented
  
imports: import math
  import tempfile
  import warnings
  from collections.abc import Sequence
  from typing import List, Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torchaudio._internal.module_utils import deprecated
  from .filtering import highpass_biquad, treble_biquad
label_desc: Use image node_id 28 for calling a global function with example usage: apply_codec(waveform, sample_rate, format, channels_first, compression, encoding, bits_per_sample) and returns: augmented