code: def short_time_objective_intelligibility(
      preds: Tensor,
      target: Tensor,
      fs: int,
      extended: bool = False,
      keep_same_device: bool = False,
  ) -> Tensor:
      r"""Calculate STOI (Short-Time Objective Intelligibility) metric for evaluating speech signals.
  
      Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to
      additive noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The
      STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative
      to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in
      the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech
      intelligibility. Description taken from  `Cees Taal's website`_ and for further details see `STOI ref1`_ and
      `STOI ref2`_.
  
      This metric is a wrapper for the `pystoi package`_. As the implementation backend implementation only supports
      calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being
      moved back to the original device.
  
      .. note:: using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
          torchmetrics[audio]`` or ``pip install pystoi``
  
      Args:
          preds: float tensor with shape ``(...,time)``
          target: float tensor with shape ``(...,time)``
          fs: sampling frequency (Hz)
          extended: whether to use the extended STOI described in `STOI ref3`_.
          keep_same_device: whether to move the stoi value to the device of preds
  
      Returns:
          stoi value of shape [...]
  
      Raises:
          ModuleNotFoundError:
              If ``pystoi`` package is not installed
          RuntimeError:
              If ``preds`` and ``target`` does not have the same shape
  
      Example:
          >>> import torch
          >>> from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
          >>> g = torch.manual_seed(1)
          >>> preds = torch.randn(8000)
          >>> target = torch.randn(8000)
          >>> short_time_objective_intelligibility(preds, target, 8000).float()
          tensor(-0.0100)
  
      """
      if not _PYSTOI_AVAILABLE:
          raise ModuleNotFoundError(
              "ShortTimeObjectiveIntelligibility metric requires that `pystoi` is installed."
              " Either install as `pip install torchmetrics[audio]` or `pip install pystoi`."
          )
      from pystoi import stoi as stoi_backend
  
      _check_same_shape(preds, target)
  
      if len(preds.shape) == 1:
          stoi_val_np = stoi_backend(
              target.detach().cpu().numpy(),
              preds.detach().cpu().numpy(),
              fs,
              extended,
          )
          stoi_val = torch.tensor(stoi_val_np)
      else:
          preds_np = (
              preds.reshape(-1, preds.shape[-1]).detach().cpu().numpy()
          )
          target_np = (
              target.reshape(-1, preds.shape[-1]).detach().cpu().numpy()
          )
          stoi_val_np = np.empty(shape=(preds_np.shape[0]))
          for b in range(preds_np.shape[0]):
              stoi_val_np[b] = stoi_backend(
                  target_np[b, :], preds_np[b, :], fs, extended
              )
          stoi_val = torch.from_numpy(stoi_val_np)
          stoi_val = stoi_val.reshape(preds.shape[:-1])
  
      if keep_same_device:
          return stoi_val.to(preds.device)
  
      return stoi_val
  
imports: import numpy
  import torch
  from torch import Tensor
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.imports import _PYSTOI_AVAILABLE
label_desc: Use image node_id 1 for calling a global function with example usage: short_time_objective_intelligibility(preds, target, fs, extended, keep_same_device) and returns: stoi_val, stoi_val