code: def _spearman_corrcoef_update(
      preds: Tensor, target: Tensor, num_outputs: int
  ) -> Tuple[Tensor, Tensor]:
      """Update and returns variables required to compute Spearman Correlation Coefficient.
  
      Check for same shape and type of input tensors.
  
      Args:
          preds: Predicted tensor
          target: Ground truth tensor
          num_outputs: Number of outputs in multioutput setting
  
      """
      if not (preds.is_floating_point() and target.is_floating_point()):
          raise TypeError(
              "Expected `preds` and `target` both to be floating point tensors, but got {pred.dtype} and {target.dtype}"
          )
      _check_same_shape(preds, target)
      _check_data_shape_to_num_outputs(preds, target, num_outputs)
  
      return preds, target
  
file_path: torchmetrics/src/torchmetrics/functional/regression/spearman.py
imports: from typing import Tuple
  import torch
  from torch import Tensor
  from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
  from torchmetrics.utilities.checks import _check_same_shape
label_desc: Use image node_id 3 for calling a global function with example usage: _spearman_corrcoef_update(preds, target, num_outputs) and returns: preds,  target