old_contents: from sklearn import datasets, svm, metrics
  import sklearn
  
  from config import opt
  def load_data():
          # The digits dataset
          iris = datasets.load_iris()
          # print(iris.)
          n_samples = len(iris.data)
          x = iris.data.reshape((n_samples, -1))
          y = iris.target
  
          x, y = sklearn.utils.shuffle(x, y)
  
          dev_idx = n_samples // 10 * 8
          test_idx = n_samples // 10 * 9
  
          train_data = (x[:dev_idx], y[:dev_idx])
          dev_data = (x[dev_idx:test_idx], y[dev_idx:test_idx])
          test_data = (x[test_idx:], y[test_idx:])
          opt.state_size = 10
          opt.data_len = len(train_data[0])
          return train_data, dev_data, test_data
  
  # def load_data():
  #         # The digits dataset
  #         digits = datasets.load_digits()
  #         n_samples = len(digits.images)
  #         x = digits.images.reshape((n_samples, -1))
  #         y = digits.target
  
  #         x, y = sklearn.utils.shuffle(x, y)
  
  #         dev_idx = n_samples // 10 * 5
  #         test_idx = n_samples // 10 * 7
  
  #         train_data = (x[:dev_idx], y[:dev_idx])
  #         dev_data = (x[dev_idx:test_idx], y[dev_idx:test_idx])
  #         test_data = (x[test_idx:], y[test_idx:])
  #         opt.state_size = 10
  #         opt.data_len = len(train_data[0])
  #         return train_data, dev_data, test_data
  
new_contents: from sklearn import datasets, svm, metrics
  import sklearn
  
  from config import opt
  def load_data():
          # The digits dataset
          # iris = datasets.load_iris()
          # n_samples = len(iris.data)
          # x = iris.data.reshape((n_samples, -1))
          # y = iris.target
  
          # x, y = sklearn.utils.shuffle(x, y)
  
          # dev_idx = n_samples // 10 * 8
          # test_idx = n_samples // 10 * 9
  
          # train_data = (x[:dev_idx], y[:dev_idx])
          # dev_data = (x[dev_idx:test_idx], y[dev_idx:test_idx])
          # test_data = (x[test_idx:], y[test_idx:])
          # opt.state_size = 10
          # opt.data_len = len(train_data[0])
          # return train_data, dev_data, test_data
  
  def load_data():
          # The digits dataset
          digits = datasets.load_digits()
          n_samples = len(digits.images)
          x = digits.images.reshape((n_samples, -1))
          y = digits.target
  
          x, y = sklearn.utils.shuffle(x, y)
  
          dev_idx = n_samples // 10 * 5
          test_idx = n_samples // 10 * 7
  
          train_data = (x[:dev_idx], y[:dev_idx])
          dev_data = (x[dev_idx:test_idx], y[dev_idx:test_idx])
          test_data = (x[test_idx:], y[test_idx:])
          opt.state_size = 10
          opt.data_len = len(train_data[0])
          return train_data, dev_data, test_data
  
subject: correct dataset loader