code: def _auc_format_inputs(x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:
      """Check that auc input is correct."""
      x = x.squeeze() if x.ndim > 1 else x
      y = y.squeeze() if y.ndim > 1 else y
  
      if x.ndim > 1 or y.ndim > 1:
          raise ValueError(
              f"Expected both `x` and `y` tensor to be 1d, but got tensors with dimension {x.ndim} and {y.ndim}"
          )
      if x.numel() != y.numel():
          raise ValueError(
              f"Expected the same number of elements in `x` and `y` tensor but received {x.numel()} and {y.numel()}"
          )
      return x, y
  
file_path: torchmetrics/src/torchmetrics/utilities/compute.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _auc_format_inputs(x, y) and returns: x,  y