code: def dcshift(
      waveform: Tensor,
      shift: float,
      limiter_gain: Optional[float] = None,
  ) -> Tensor:
      r"""Apply a DC shift to the audio. Similar to SoX implementation.
  
      .. devices:: CPU CUDA
  
      .. properties:: TorchScript
  
      This can be useful to remove a DC offset
      (caused perhaps by a hardware problem in the recording chain) from the audio
  
      Args:
          waveform (Tensor): audio waveform of dimension of `(..., time)`
          shift (float): indicates the amount to shift the audio
              Allowed range of values for shift : -2.0 to +2.0
          limiter_gain (float of None, optional): It is used only on peaks to prevent clipping
              It should have a value much less than 1 (e.g. 0.05 or 0.02)
  
      Returns:
          Tensor: Waveform of dimension of `(..., time)`
  
      Reference:
          - http://sox.sourceforge.net/sox.html
      """
      output_waveform = waveform
      limiter_threshold = 0.0
  
      if limiter_gain is not None:
          limiter_threshold = 1.0 - (abs(shift) - limiter_gain)
  
      # Note:
      # the following index-based update breaks auto-grad support
      if limiter_gain is not None and shift > 0:
          mask = waveform > limiter_threshold
          temp = (
              (waveform[mask] - limiter_threshold)
              * limiter_gain
              / (1 - limiter_threshold)
          )
          output_waveform[mask] = (
              temp + limiter_threshold + shift
          ).clamp(max=limiter_threshold)
          output_waveform[~mask] = (waveform[~mask] + shift).clamp(
              min=-1, max=1
          )
      elif limiter_gain is not None and shift < 0:
          mask = waveform < -limiter_threshold
          temp = (
              (waveform[mask] + limiter_threshold)
              * limiter_gain
              / (1 - limiter_threshold)
          )
          output_waveform[mask] = (
              temp - limiter_threshold + shift
          ).clamp(min=-limiter_threshold)
          output_waveform[~mask] = (waveform[~mask] + shift).clamp(
              min=-1, max=1
          )
      else:
          output_waveform = (waveform + shift).clamp(min=-1, max=1)
  
      return output_waveform
  
imports: import math
  import warnings
  from typing import Optional
  import torch
  from torch import Tensor
  from torchaudio._extension import _IS_TORCHAUDIO_EXT_AVAILABLE
label_desc: Use image node_id 10 for calling a global function with example usage: dcshift(waveform, shift, limiter_gain) and returns: output_waveform