code: def completeness_score(preds: Tensor, target: Tensor) -> Tensor:
      """Compute the Completeness score between two clusterings.
  
      Args:
          preds: predicted cluster labels
          target: ground truth cluster labels
  
      Returns:
          scalar tensor with the rand score
  
      Example:
          >>> from torchmetrics.functional.clustering import completeness_score
          >>> import torch
          >>> completeness_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0]))
          tensor(1.)
          >>> completeness_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
          tensor(0.6667)
  
      """
      completeness, _ = _completeness_score_compute(preds, target)
      return 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 4 for calling a global function with example usage: completeness_score(preds, target) and returns: completeness