code: def _load(train_file, test_file, name):
      if not os.path.exists(train_file) or (
          test_file is not None and not os.path.exists(test_file)
      ):
          raise IOError(
              "Dataset missing! "
              + "Run 'make download-%s' at the project root." % name
          )
  
      if test_file:
          return load_svmlight_files((train_file, test_file))
      else:
          X, y = load_svmlight_files((train_file,))
          return X, y, None, None
  
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 2 for calling a global function with example usage: _load(train_file, test_file, name) and returns: load_svmlight_files, X,  y,  None,  None