code: def num_samples(y_true, y_pred=None, sample_weight=None):
      """Compute the number of samples.
  
      Args:
          y_true (array-like): Ground truth (correct) target values.
          y_pred (array-like): Estimated targets. Ignored.
          sample_weight (array-like, optional): Sample weights.
  
      Returns:
          float: (Weighted) number of samples.
      """
      sample_weight = check_inputs(
          y_true, y_true, sample_weight, ensure_2d=False
      )[2]
      return sum(sample_weight)
  
imports: from itertools import permutations
  from typing import Union
  import warnings
  import numpy
  import pandas
  from scipy.special import rel_entr
  from sklearn.metrics import make_scorer, recall_score, precision_score
  from sklearn.metrics import multilabel_confusion_matrix
  from sklearn.metrics._classification import _prf_divide, _check_zero_division
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_X_y
  from aif360.metrics import ot_metric
  from aif360.sklearn.utils import check_inputs, check_groups
  from aif360.detectors.mdss.ScoringFunctions import BerkJones, Bernoulli
  from aif360.detectors.mdss.MDSS import MDSS
label_desc: Use image node_id 6 for calling a global function with example usage: num_samples(y_true, y_pred, sample_weight) and returns: sum