code: def acton_QBC(data_path, n_queries):
      # acton has no SVM support, so the LogisticRegression model is used
      acton_main(
          data_path=data_path,
          feature_cols=["feat01", "feat02", "feat03", "feat04"],
          label_col="label",
          output_path="out.csv",
          n_epochs=n_queries,
          initial_count=3,
          recommender="QBCRecommender",
          predictor="LogisticRegressionCommittee",
      )
  
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 9 for calling a global function with example usage: acton_QBC(data_path, n_queries) without return types