code: def test_classification_dataset(self):
      X, y, split = functions.get_sample_dataset(
          self.dataset_properties
      )
      assert X.shape == (100, 20)
      assert y.shape == (100,)
      assert len(np.unique(y)) == 2
  
      self.dataset_properties["n_classes"] = 4
      self.dataset_properties["n_informative"] = 18
      X, y, split = functions.get_sample_dataset(
          self.dataset_properties
      )
      assert X.shape == (100, 20)
      assert y.shape == (100,)
      assert len(np.unique(y)) == 4
  
      self.dataset_properties["n_features"] = 100
      X, y, split = functions.get_sample_dataset(
          self.dataset_properties
      )
      assert X.shape == (100, 100)
      assert y.shape == (100,)
      assert len(np.unique(y)) == 4
  
      self.dataset_properties["n_samples"] = 24
      X, y, split = functions.get_sample_dataset(
          self.dataset_properties
      )
      assert X.shape == (24, 100)
      assert y.shape == (24,)
      assert len(np.unique(y)) == 4
  
imports: from __future__ import absolute_import, print_function, division, unicode_literals
  import unittest
  import os
  import numpy
  from xcessiv import functions, exceptions
  from sklearn.datasets import load_digits
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.decomposition import PCA
  from sklearn.pipeline import Pipeline
  import pickle
label_desc: Use image node_id 2 for calling the GetSampleDataset obj's underlying member method code with example usage: obj.test_classification_dataset() without return types