def test_set_scalar_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(np.zeros((10, 10))))
    orig_X_val = adata.X.copy()
    subset_idx = slice_subset(adata.obs_names)

    adata_subset = adata[subset_idx, :]

    adata_subset.X = 1

    assert adata_subset.is_view
    assert np.all(asarray(adata[subset_idx, :].X) == 1)

    assert asarray((orig_X_val != adata.X)).sum() == mul(*adata_subset.shape)
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)