code: def biquad(
      waveform: Tensor,
      b0: float,
      b1: float,
      b2: float,
      a0: float,
      a1: float,
      a2: float,
  ) -> Tensor:
      r"""Perform a biquad filter of input tensor.  Initial conditions set to 0.
  
      .. devices:: CPU CUDA
  
      .. properties:: Autograd TorchScript
  
      Args:
          waveform (Tensor): audio waveform of dimension of `(..., time)`
          b0 (float or torch.Tensor): numerator coefficient of current input, x[n]
          b1 (float or torch.Tensor): numerator coefficient of input one time step ago x[n-1]
          b2 (float or torch.Tensor): numerator coefficient of input two time steps ago x[n-2]
          a0 (float or torch.Tensor): denominator coefficient of current output y[n], typically 1
          a1 (float or torch.Tensor): denominator coefficient of current output y[n-1]
          a2 (float or torch.Tensor): denominator coefficient of current output y[n-2]
  
      Returns:
          Tensor: Waveform with dimension of `(..., time)`
  
      Reference:
         - https://en.wikipedia.org/wiki/Digital_biquad_filter
      """
  
      device = waveform.device
      dtype = waveform.dtype
  
      b0 = torch.as_tensor(b0, dtype=dtype, device=device).view(1)
      b1 = torch.as_tensor(b1, dtype=dtype, device=device).view(1)
      b2 = torch.as_tensor(b2, dtype=dtype, device=device).view(1)
      a0 = torch.as_tensor(a0, dtype=dtype, device=device).view(1)
      a1 = torch.as_tensor(a1, dtype=dtype, device=device).view(1)
      a2 = torch.as_tensor(a2, dtype=dtype, device=device).view(1)
  
      output_waveform = lfilter(
          waveform,
          torch.cat([a0, a1, a2]),
          torch.cat([b0, b1, b2]),
      )
      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 8 for calling a global function with example usage: biquad(waveform, b0, b1, b2, a0, a1, a2) and returns: output_waveform