code: def simple_classification(
      n_samples=100, n_features=10, random_state=33
  ):
      """
      Generate simple classification task for training.
  
      Parameters
      ----------
      n_samples : int
          Number of samples in dataset.
      n_features : int
          Number of features for each sample.
      random_state : int
          Random state to make results reproducible.
  
      Returns
      -------
      tuple
          Returns tuple that contains 4 variables. There are input train,
          input test, target train, target test respectevly.
      """
      X, y = datasets.make_classification(
          n_samples=n_samples,
          n_features=n_features,
          random_state=random_state,
      )
      shuffle_split = StratifiedShuffleSplit(
          n_splits=1,
          train_size=0.6,
          test_size=0.1,
          random_state=random_state,
      )
  
      train_index, test_index = next(shuffle_split.split(X, y))
      x_train, x_test = X[train_index], X[test_index]
      y_train, y_test = y[train_index], y[test_index]
  
      return x_train, x_test, y_train, y_test
  
file_path: neupy/tests/helpers.py
imports: import sys
  from contextlib import contextmanager
  import six
  import numpy
  import pandas
  from sklearn import datasets
  from sklearn.model_selection import StratifiedShuffleSplit
  from matplotlib import pyplot
  from neupy import algorithms, layers, utils, init
  from neupy.storage import save_dict, load_dict
label_desc: Use image node_id 1 for calling a global function with example usage: simple_classification(n_samples, n_features, random_state) and returns: x_train,  x_test,  y_train,  y_test