code: def completeness_score(labels_true, labels_pred):
      """Compute completeness metric of a cluster labeling given a ground truth.
  
      A clustering result satisfies completeness if all the data points
      that are members of a given class are elements of the same cluster.
  
      This metric is independent of the absolute values of the labels:
      a permutation of the class or cluster label values won't change the
      score value in any way.
  
      This metric is not symmetric: switching ``label_true`` with ``label_pred``
      will return the :func:`homogeneity_score` which will be different in
      general.
  
      Read more in the :ref:`User Guide <homogeneity_completeness>`.
  
      Parameters
      ----------
      labels_true : array-like of shape (n_samples,)
          Ground truth class labels to be used as a reference.
  
      labels_pred : array-like of shape (n_samples,)
          Cluster labels to evaluate.
  
      Returns
      -------
      completeness : float
         Score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling.
  
      See Also
      --------
      homogeneity_score : Homogeneity metric of cluster labeling.
      v_measure_score : V-Measure (NMI with arithmetic mean option).
  
      References
      ----------
  
      .. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
         conditional entropy-based external cluster evaluation measure
         <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
  
      Examples
      --------
  
      Perfect labelings are complete::
  
        >>> from sklearn.metrics.cluster import completeness_score
        >>> completeness_score([0, 0, 1, 1], [1, 1, 0, 0])
        1.0
  
      Non-perfect labelings that assign all classes members to the same clusters
      are still complete::
  
        >>> print(completeness_score([0, 0, 1, 1], [0, 0, 0, 0]))
        1.0
        >>> print(completeness_score([0, 1, 2, 3], [0, 0, 1, 1]))
        0.999...
  
      If classes members are split across different clusters, the
      assignment cannot be complete::
  
        >>> print(completeness_score([0, 0, 1, 1], [0, 1, 0, 1]))
        0.0
        >>> print(completeness_score([0, 0, 0, 0], [0, 1, 2, 3]))
        0.0
      """
      return homogeneity_completeness_v_measure(
          labels_true, labels_pred
      )[1]
  
imports: import warnings
  from math import log
  from numbers import Real
  import numpy
  from scipy import sparse
  from ...utils._param_validation import Interval, StrOptions, validate_params
  from ...utils.multiclass import type_of_target
  from ...utils.validation import check_array, check_consistent_length
  from ._expected_mutual_info_fast import expected_mutual_information
label_desc: Use image node_id 9 for calling a global function with example usage: completeness_score(labels_true, labels_pred) and returns: homogeneity_completeness_v_measure