old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer.functions.array import separate
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'shape': (2, 3, 4), 'axis': 0},
          {'shape': (2, 3, 4), 'axis': 1},
          {'shape': (2, 3, 4), 'axis': 2},
          {'shape': (2, 3, 4), 'axis': -1},
          {'shape': (2, 3, 4), 'axis': -3},
          {'shape': (2,), 'axis': 0},
          {'shape': (2,), 'axis': -1},
      ],
      [
          {'dtype': numpy.float16},
          {'dtype': numpy.float32},
          {'dtype': numpy.float64},
      ]
  ))
  class TestSeparate(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
          yshape = list(self.shape)
          del yshape[self.axis]
          self.gys = [numpy.random.uniform(-1, 1, yshape).astype(self.dtype)
                      for _ in range(self.shape[self.axis])]
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          ys = separate.separate(x, self.axis)
  
          self.assertIsInstance(ys, tuple)
          self.assertEqual(len(ys), self.shape[self.axis])
          for i in range(self.shape[self.axis]):
              expect = self.x.take(i, axis=self.axis)
              testing.assert_allclose(ys[i].data, expect)
  
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x))
  
      def check_backward(self, x_data, gys_data):
          def f(x):
              return separate.separate(x, self.axis)
  
          gradient_check.check_backward(f, x_data, gys_data, dtype='d')
  
      def test_backward_cpu(self):
          self.check_backward(self.x, self.gys)
  
      @attr.gpu
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x),
                              [cuda.to_gpu(g) for g in self.gys])
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import numpy
  
  from chainer import functions
  from chainer import testing
  from chainer.utils import force_array
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'shape': (2, 3, 4), 'axis': 0},
          {'shape': (2, 3, 4), 'axis': 1},
          {'shape': (2, 3, 4), 'axis': 2},
          {'shape': (2, 3, 4), 'axis': -1},
          {'shape': (2, 3, 4), 'axis': -3},
          {'shape': (2,), 'axis': 0},
          {'shape': (2,), 'axis': -1},
      ],
      [
          {'dtype': numpy.float16},
          {'dtype': numpy.float32},
          {'dtype': numpy.float64},
      ]
  ))
  @testing.fix_random()
  @testing.inject_backend_tests(
      None,
      # CPU tests
      [
          {},
      ]
      # GPU tests
      + testing.product({
          'use_cuda': [True],
          'use_cudnn': ['never', 'always'],
          'cuda_device': [0, 1],
      })
      # ChainerX tests
      + testing.product({
          'use_chainerx': [True],
          'chainerx_device': ['native:0', 'cuda:0', 'cuda:1'],
      })
  )
  class TestSeparate(testing.FunctionTestCase):
  
      def setUp(self):
          self.skip_double_backward_test = True
  
      def generate_inputs(self):
          x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
          return x,
  
      def forward(self, inputs, device):
          x, = inputs
          return functions.separate(x, self.axis)
  
      def forward_expected(self, inputs):
          x, = inputs
          return tuple(
              force_array(x.take(i, axis=self.axis))
              for i in range(self.shape[self.axis])
          )
  
  
  testing.run_module(__name__, __file__)
  
subject: Simplify F.separate test