code: def _multilabel_ranking_tensor_validation(
      preds: Tensor,
      target: Tensor,
      num_labels: int,
      ignore_index: Optional[int] = None,
  ) -> None:
      _multilabel_confusion_matrix_tensor_validation(
          preds, target, num_labels, ignore_index
      )
      if not preds.is_floating_point():
          raise ValueError(
              f"Expected preds tensor to be floating point, but received input with dtype {preds.dtype}"
          )
  
file_path: torchmetrics/src/torchmetrics/functional/classification/ranking.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torchmetrics.functional.classification.confusion_matrix import _multilabel_confusion_matrix_arg_validation, _multilabel_confusion_matrix_format, _multilabel_confusion_matrix_tensor_validation
  from torchmetrics.utilities.data import _cumsum
label_desc: Use image node_id 3 for calling a global function with example usage: _multilabel_ranking_tensor_validation(preds, target, num_labels, ignore_index) without return types