code: from sklearn import datasets, svm, metrics
  from sklearn.model_selection import train_test_split
  
  
  if __name__ == '__main__':
      digits = datasets.load_digits()
  
      # flatten the images
      n_samples = len(digits.images)
      data = digits.images.reshape((n_samples, -1))
  
      # Create a classifier: a support vector classifier
      clf = svm.SVC(gamma=0.001)
  
      # Split data into 50% train and 50% test subsets
      X_train, X_test, y_train, y_test = train_test_split(
          data, digits.target, test_size=0.5, shuffle=False
      )
  
      # Learn the digits on the train subset
      clf.fit(X_train, y_train)
  
      # Predict the value of the digit on the test subset
      predicted = clf.predict(X_test)
      
      print('hello cloud come on')	
      print('lil bitch')
      print(
          f"Classification report for classifier {clf}:\n"
          f"{metrics.classification_report(y_test, predicted)}\n"
      )
  
  
apis: sklearn.model_selection.train_test_split sklearn.metrics.classification_report sklearn.datasets.load_digits sklearn.svm.SVC