code: def modAL_uncertainty(X, y, n_queries):
      modAL_learner = ActiveLearner(
          LogisticRegression(
              solver="liblinear", n_jobs=1, multi_class="ovr"
          ),
          X_training=X[[0, 50, 100]],
          y_training=y[[0, 50, 100]],
      )
  
      for _ in range(n_queries):
          query_idx, query_inst = modAL_learner.query(X)
          modAL_learner.teach(X[query_idx], y[query_idx])
  
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 5 for calling a global function with example usage: modAL_uncertainty(X, y, n_queries) without return types