code: def test_transform(self):
      dataset = dummy_dataset.DummyDataset(
          mode=self.in_mode, return_array=True, convert=True
      )
  
      def transform(*args, **kwargs):
          if self.in_mode is tuple:
              self.assertEqual(len(args), 3)
              self.assertEqual(len(kwargs), 0)
              a, b, c = args
          elif self.in_mode is dict:
              self.assertEqual(len(args), 0)
              self.assertEqual(len(kwargs), 3)
              a, b, c = kwargs["a"], kwargs["b"], kwargs["c"]
          elif self.in_mode is None:
              self.assertEqual(len(args), 1)
              self.assertEqual(len(kwargs), 0)
              (a,) = args
              b, c = a, a
  
          if self.with_batch:
              self.assertIsInstance(a, np.ndarray)
              self.assertIsInstance(b, np.ndarray)
              self.assertIsInstance(c, np.ndarray)
          else:
              self.assertIsInstance(a, float)
              self.assertIsInstance(b, float)
              self.assertIsInstance(c, float)
  
          if self.out_mode is tuple:
              return a + b, b + c
          elif self.out_mode is dict:
              return {"alpha": a + b, "beta": b + c}
          elif self.out_mode is None:
              return a + b + c
  
      if self.in_mode is not None:
          a, b, c = dataset.data
      else:
          (a,) = dataset.data
          b, c = a, a
  
      if self.out_mode is not None:
          if self.with_batch:
              view = dataset.transform_batch(
                  ("alpha", "beta"), transform
              )
          else:
              view = dataset.transform(("alpha", "beta"), transform)
          data = np.vstack((a + b, b + c))
      else:
          if self.with_batch:
              view = dataset.transform_batch("alpha", transform)
          else:
              view = dataset.transform("alpha", transform)
          data = (a + b + c)[None]
  
      self.assertIsInstance(view, chainer.dataset.TabularDataset)
      self.assertEqual(len(view), len(dataset))
  
      if self.out_mode is not None:
          self.assertEqual(view.keys, ("alpha", "beta"))
          self.assertEqual(view.mode, self.out_mode)
      else:
          self.assertEqual(view.keys, ("alpha",))
          self.assertEqual(view.mode, self.out_mode)
  
      output = view.get_examples(self.indices, self.key_indices)
  
      if self.indices is not None:
          data = data[:, self.indices]
      if self.key_indices is not None:
          data = data[list(self.key_indices)]
  
      for out, d in six.moves.zip_longest(output, data):
          np.testing.assert_equal(out, d)
          if self.with_batch:
              self.assertIsInstance(out, np.ndarray)
          else:
              self.assertIsInstance(out, list)
  
      self.assertEqual(view.convert(view.fetch()), "converted")
  
file_path: chainer/tests/chainer_tests/dataset_tests/tabular_tests/test_transform.py
imports: import unittest
  import numpy
  import six
  import chainer
  from chainer import testing
  from chainer_tests.dataset_tests.tabular_tests import dummy_dataset
label_desc: Use image node_id 1 for calling the TestTransform obj's underlying member method code with example usage: obj.test_transform() and returns: unknown,  unknown, dict, unknown