def test_set_obs(adata, subset_func):
    init_hash = joblib.hash(adata)

    subset = adata[subset_func(adata.obs_names), :]

    new_obs = pd.DataFrame(
        dict(a=np.ones(subset.n_obs), b=np.ones(subset.n_obs)),
        index=subset.obs_names,
    )

    assert subset.is_view
    subset.obs = new_obs
    assert not subset.is_view
    assert np.all(subset.obs == new_obs)

    assert joblib.hash(adata) == init_hash
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