code: def mkl_multiclass(
      fm_train_real,
      fm_test_real,
      label_train_multiclass,
      width,
      C,
      epsilon,
      num_threads,
      mkl_epsilon,
      mkl_norm,
  ):
      from shogun import MulticlassLabels
      import shogun as sg
  
      kernel = sg.create_kernel("CombinedKernel")
      feats_train = sg.create_features("CombinedFeatures")
      feats_test = sg.create_features("CombinedFeatures")
  
      subkfeats_train = sg.create_features(fm_train_real)
      subkfeats_test = sg.create_features(fm_test_real)
      subkernel = sg.create_kernel("GaussianKernel", width=width)
      feats_train.add("feature_array", subkfeats_train)
      feats_test.add("feature_array", subkfeats_test)
      kernel.add("kernel_array", subkernel)
  
      subkfeats_train = sg.create_features(fm_train_real)
      subkfeats_test = sg.create_features(fm_test_real)
      subkernel = sg.create_kernel("LinearKernel")
      feats_train.add("feature_array", subkfeats_train)
      feats_test.add("feature_array", subkfeats_test)
      kernel.add("kernel_array", subkernel)
  
      subkfeats_train = sg.create_features(fm_train_real)
      subkfeats_test = sg.create_features(fm_test_real)
      subkernel = sg.create_kernel(
          "PolyKernel", cache_size=10, degree=2
      )
      feats_train.add("feature_array", subkfeats_train)
      feats_test.add("feature_array", subkfeats_test)
      kernel.add("kernel_array", subkernel)
  
      kernel.init(feats_train, feats_train)
  
      labels = MulticlassLabels(label_train_multiclass)
  
      mkl = sg.create_machine(
          "MKLMulticlass",
          C=C,
          kernel=kernel,
          labels=labels,
          mkl_eps=mkl_epsilon,
          mkl_norm=mkl_norm,
      )
  
      mkl.get("machine").put("epsilon", epsilon)
  
      mkl.get_global_parallel().set_num_threads(num_threads)
  
      mkl.train()
  
      kernel.init(feats_train, feats_test)
  
      out = mkl.apply().get("labels")
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: mkl_multiclass(fm_train_real, fm_test_real, label_train_multiclass, width, C, epsilon, num_threads, mkl_epsilon, mkl_norm) and returns: out