code: def classifier_featureblock_logistic_regression(
      fm_train=traindat, fm_test=testdat, label_train=label_traindat
  ):
      import shogun as sg
      from shogun import BinaryLabels, IndexBlock, IndexBlockGroup
  
      try:
          from shogun import FeatureBlockLogisticRegression
      except ImportError:
          print("FeatureBlockLogisticRegression not available")
          exit(0)
  
      features = sg.create_features(hstack((traindat, traindat)))
      labels = BinaryLabels(hstack((label_train, label_train)))
  
      n_features = features.get("num_features")
      block_one = IndexBlock(0, n_features // 2)
      block_two = IndexBlock(n_features // 2, n_features)
      block_group = IndexBlockGroup()
      block_group.add_block(block_one)
      block_group.add_block(block_two)
  
      mtlr = FeatureBlockLogisticRegression(
          0.1, features, labels, block_group
      )
      mtlr.set_regularization(1)  # use regularization ratio
      mtlr.set_tolerance(1e-2)  # use 1e-2 tolerance
      mtlr.train()
      out = mtlr.apply().get_labels()
  
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: classifier_featureblock_logistic_regression(fm_train, fm_test, label_train) and returns: out