code: def load_usps_noisy():
      X_train, y_train, X_test, y_test = load_usps()
      n_samples = X_train.shape[0]
      n = n_samples / 10
      random_state = check_random_state(0)
      indices = np.arange(n_samples)
      random_state.shuffle(indices)
      indices2 = np.arange(n_samples)
      random_state.shuffle(indices2)
      y_train[indices[:n]] = y_train[indices2[:n]]
      return X_train, y_train, X_test, y_test
  
imports: import os
  import numpy
  import scipy.sparse
  from sklearn.datasets.base import get_data_home
  from sklearn.utils import check_random_state
label_desc: Use image node_id 33 for calling a global function with example usage: load_usps_noisy() and returns: X_train,  y_train,  X_test,  y_test