code: def run_in_cluster():
      dataset = datasets.fetch_covtype(data_home=tempfile.mkdtemp())
      X, y = dataset.data, dataset.target - 1
      training_size = 400000
      max_depth = 10
      clf = DecisionTreeClassifier(max_depth=max_depth)
      start = time.time()
      clf.fit(X[:training_size], y[:training_size])
      end = time.time()
      y_pred = clf.predict(X[training_size:])
      accuracy = metrics.accuracy_score(y[training_size:], y_pred)
      return end - start, accuracy
  
imports: import numpy
  import ray
  from sklearn import datasets, metrics
  import time
  import tempfile
  import os
  import json
label_desc: Use image node_id 7 for calling a global function with example usage: run_in_cluster() and returns: unknown,  accuracy