code: def _validate_intrinsic_cluster_data(
      data: Tensor, labels: Tensor
  ) -> None:
      """Validate that the input data and labels have correct shape and type."""
      if data.ndim != 2:
          raise ValueError(
              f"Expected 2D data, got {data.ndim}D data instead"
          )
      if not data.is_floating_point():
          raise ValueError(
              f"Expected floating point data, got {data.dtype} data instead"
          )
      if labels.ndim != 1:
          raise ValueError(
              f"Expected 1D labels, got {labels.ndim}D labels instead"
          )
  
file_path: torchmetrics/src/torchmetrics/functional/clustering/utils.py
imports: from typing import Optional, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
label_desc: Use image node_id 8 for calling a global function with example usage: _validate_intrinsic_cluster_data(data, labels) without return types