def test_view_delattr(attr, subset_func):
    base = gen_adata((10, 10))
    orig_hash = joblib.hash(base)
    subset = base[subset_func(base.obs_names), subset_func(base.var_names)]
    empty = ad.AnnData(obs=subset.obs[[]], var=subset.var[[]])

    delattr(subset, attr)

    assert not subset.is_view
    # Should now have same value as default
    assert_equal(getattr(subset, attr), getattr(empty, attr))
    assert orig_hash == joblib.hash(base)  # Original should not be modified
def deeplift_grad(module, grad_input, grad_output):
    """The backward hook which computes the deeplift
    gradient for an nn.Module
    """
    # first, get the module type
    module_type = module.__class__.__name__
    # first, check the module is supported
    if module_type in op_handler:
        if op_handler[module_type].__name__ not in ['passthrough', 'linear_1d']:
            return op_handler[module_type](module, grad_input, grad_output)
    else:
        print('Warning: unrecognized nn.Module: {}'.format(module_type))
        return grad_input