def test_view_of_view_modification():
    adata = ad.AnnData(np.zeros((10, 10)))
    adata[0, :][:, 5:].X = np.ones(5)
    assert np.all(adata.X[0, 5:] == np.ones(5))
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2))
    assert np.all(adata.X[1:3, 1:3] == np.ones((2, 2)))

    adata.X = sparse.csr_matrix(adata.X)
    adata[0, :][:, 5:].X = np.ones(5) * 2
    assert np.all(asarray(adata.X)[0, 5:] == np.ones(5) * 2)
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2)) * 2
    assert np.all(asarray(adata.X)[1:3, 1:3] == np.ones((2, 2)) * 2)
def testWrapperWeights(self, wrapper):
    """Tests that wrapper weights contain wrapped cells weights."""
    base_cell = layers.SimpleRNNCell(1, name="basic_rnn_cell")
    rnn_cell = wrapper(base_cell)
    rnn_layer = layers.RNN(rnn_cell)
    inputs = ops.convert_to_tensor_v2_with_dispatch([[[1]]],
                                                    dtype=dtypes.float32)
    rnn_layer(inputs)

    wrapper_name = generic_utils.to_snake_case(wrapper.__name__)
    expected_weights = ["rnn/" + wrapper_name + "/" + var for var in
                        ("kernel:0", "recurrent_kernel:0", "bias:0")]
    self.assertLen(rnn_cell.weights, 3)
    self.assertCountEqual([v.name for v in rnn_cell.weights], expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.trainable_variables],
                          expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.non_trainable_variables],
                          [])
    self.assertCountEqual([v.name for v in rnn_cell.cell.weights],
                          expected_weights)