code: def v_measure_score(
      preds: Tensor, target: Tensor, beta: float = 1.0
  ) -> Tensor:
      """Compute the V-measure score between two clusterings.
  
      Args:
          preds: predicted cluster labels
          target: ground truth cluster labels
          beta: weight of the harmonic mean between homogeneity and completeness
  
      Returns:
          scalar tensor with the rand score
  
      Example:
          >>> from torchmetrics.functional.clustering import v_measure_score
          >>> import torch
          >>> v_measure_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0]))
          tensor(1.)
          >>> v_measure_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
          tensor(0.8000)
  
      """
      completeness, homogeneity = _completeness_score_compute(
          preds, target
      )
      if homogeneity + completeness == 0.0:
          return torch.ones_like(homogeneity)
      return (
          (1 + beta)
          * homogeneity
          * completeness
          / (beta * homogeneity + completeness)
      )
  
file_path: torchmetrics/src/torchmetrics/functional/clustering/homogeneity_completeness_v_measure.py
imports: from typing import Tuple
  import torch
  from torch import Tensor
  from torchmetrics.functional.clustering.mutual_info_score import mutual_info_score
  from torchmetrics.functional.clustering.utils import calculate_entropy, check_cluster_labels
label_desc: Use image node_id 5 for calling a global function with example usage: v_measure_score(preds, target, beta) and returns: unknown, torch