code: def _erb_filterbank(wave: Tensor, coefs: Tensor) -> Tensor:
      """Translated from gammatone package.
  
      Args:
          wave: shape [B, time]
          coefs: shape [N, 10]
  
      Returns:
          Tensor: shape [B, N, time]
  
      """
      from torchaudio.functional.filtering import lfilter
  
      num_batch, time = wave.shape
      wave = wave.to(dtype=coefs.dtype).reshape(
          num_batch, 1, time
      )  # [B, time]
      wave = wave.expand(-1, coefs.shape[0], -1)  # [B, N, time]
  
      gain = coefs[:, 9]
      as1 = coefs[:, (0, 1, 5)]  # A0, A11, A2
      as2 = coefs[:, (0, 2, 5)]  # A0, A12, A2
      as3 = coefs[:, (0, 3, 5)]  # A0, A13, A2
      as4 = coefs[:, (0, 4, 5)]  # A0, A14, A2
      bs = coefs[:, 6:9]  # B0, B1, B2
  
      y1 = lfilter(wave, bs, as1, batching=True)
      y2 = lfilter(y1, bs, as2, batching=True)
      y3 = lfilter(y2, bs, as3, batching=True)
      y4 = lfilter(y3, bs, as4, batching=True)
      return y4 / gain.reshape(1, -1, 1)
  
file_path: torchmetrics/src/torchmetrics/functional/audio/srmr.py
imports: from functools import lru_cache
  from math import ceil, pi
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn.functional import pad
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.imports import _GAMMATONE_AVAILABLE, _TORCHAUDIO_AVAILABLE, _TORCHAUDIO_GREATER_EQUAL_0_10
label_desc: Use image node_id 5 for calling a global function with example usage: _erb_filterbank(wave, coefs) and returns: unknown