code: def comparisons(n_queries=10):
      # loading the data
      X, y = load_iris(return_X_y=True)
  
      libact_uncertainty(X, y, n_queries)
      libact_QBC(X, y, n_queries)
      libact_EER(X, y, n_queries)
      acton_uncertainty("iris.csv", n_queries)
      acton_QBC("iris.csv", n_queries)
      alp_uncertainty(X, y, n_queries)
      alp_QBC(X, y, n_queries)
      modAL_uncertainty(X, y, n_queries)
      modAL_QBC(X, y, n_queries)
      modAL_EER(X, y, n_queries)
  
imports: from time import time
  import numpy
  from acton.acton import main
  from alp.active_learning.active_learning import ActiveLearner
  from libact.base.dataset import Dataset
  from libact.labelers import IdealLabeler
  from libact.models.logistic_regression import LogisticRegression
  from libact.query_strategies import QueryByCommittee, UncertaintySampling
  from libact.query_strategies.multiclass.expected_error_reduction import EER
  from modAL.expected_error import expected_error_reduction
  from modAL.models import ActiveLearner, Committee
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 12 for calling a global function with example usage: comparisons(n_queries) without return types