code: def transfer_multitask_clustered_logistic_regression(
      fm_train=traindat, fm_test=testdat, label_train=label_traindat
  ):
      import shogun as sg
  
      try:
          sg.create_machine("MultitaskClusteredLogisticRegression")
      except ImportError:
          print("MultitaskClusteredLogisticRegression not available")
          exit()
  
      features = sg.create_features(
          hstack((traindat, sin(traindat), cos(traindat)))
      )
      labels = sg.BinaryLabels(
          hstack((label_train, label_train, label_train))
      )
  
      n_vectors = features.get_num_vectors()
      task_one = sg.Task(0, n_vectors // 3)
      task_two = sg.Task(n_vectors // 3, 2 * n_vectors // 3)
      task_three = sg.Task(2 * n_vectors // 3, n_vectors)
      task_group = sg.TaskGroup()
      task_group.append_task(task_one)
      task_group.append_task(task_two)
      task_group.append_task(task_three)
  
      mtlr = sg.MultitaskClusteredLogisticRegression(
          1.0, 100.0, features, labels, task_group, 2
      )
      # mtlr.io.set_loglevel(MSG_DEBUG)
      mtlr.set_tolerance(1e-3)  # use 1e-2 tolerance
      mtlr.set_max_iter(100)
      mtlr.train()
      mtlr.set_current_task(0)
      # print mtlr.get_w()
      out = mtlr.apply_regression().get("labels")
  
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: transfer_multitask_clustered_logistic_regression(fm_train, fm_test, label_train) and returns: out