code: def classifier_multiclassocas(
      num_vec=10,
      num_class=3,
      distance=15,
      width=2.1,
      C=1,
      epsilon=1e-5,
      seed=1,
  ):
      from shogun import MulticlassLabels
      import shogun as sg
  
      try:
          sg.create_machine("MulticlassOCAS")
      except ImportError:
          print("MulticlassOCAS not available")
          return
  
      # reproducible results
      random.seed(seed)
  
      # generate some training data where each class pair is linearly separable
      label_train = array(
          [mod(x, num_class) for x in range(num_vec)], dtype="float64"
      )
      label_test = array(
          [mod(x, num_class) for x in range(num_vec)], dtype="float64"
      )
      fm_train = array(random.randn(num_class, num_vec))
      fm_test = array(random.randn(num_class, num_vec))
      for i in range(len(label_train)):
          fm_train[int(label_train[i]), i] += distance
          fm_test[int(label_test[i]), i] += distance
  
      feats_train = sg.create_features(fm_train)
      feats_test = sg.create_features(fm_test)
  
      labels = sg.create_labels(label_train)
  
      classifier = sg.create_machine(
          "MulticlassOCAS", labels=labels, C=C
      )
      classifier.train(feats_train)
  
      out = classifier.apply(feats_test).get("labels")
      # print label_test
      # print out
      return out, classifier
  
label_desc: Use image node_id 1 for calling a global function with example usage: classifier_multiclassocas(num_vec, num_class, distance, width, C, epsilon, seed) and returns: out,  classifier