code: def train(**kwargs):
      # The example from http://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py
      digits = datasets.load_digits()
  
      images_and_labels = list(zip(digits.images, digits.target))
      n_samples = len(digits.images)
      data = digits.images.reshape((n_samples, -1))
  
      classifier = svm.SVC(**kwargs)
  
      classifier.fit(
          data[: n_samples // 2], digits.target[: n_samples // 2]
      )
  
      expected = digits.target[n_samples // 2 :]
      predicted = classifier.predict(data[n_samples // 2 :])
  
      print(
          "Classification report for classifier %s:\n%s\n"
          % (
              classifier,
              metrics.classification_report(expected, predicted),
          )
      )
  
      accuracy = metrics.accuracy_score(expected, predicted)
      return accuracy
  
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: accuracy