code: def _cal_srmr_score(
      bw: Tensor, avg_energy: Tensor, cutoffs: Tensor
  ) -> Tensor:
      """Calculate srmr score."""
      if (cutoffs[4] <= bw) and (cutoffs[5] > bw):
          kstar = 5
      elif (cutoffs[5] <= bw) and (cutoffs[6] > bw):
          kstar = 6
      elif (cutoffs[6] <= bw) and (cutoffs[7] > bw):
          kstar = 7
      elif cutoffs[7] <= bw:
          kstar = 8
      else:
          raise ValueError(
              "Something wrong with the cutoffs compared to bw values."
          )
      return torch.sum(avg_energy[:, :4]) / torch.sum(
          avg_energy[:, 4:kstar]
      )
  
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 7 for calling a global function with example usage: _cal_srmr_score(bw, avg_energy, cutoffs) and returns: unknown