def test_drop_obs_column():
    adata = ad.AnnData(np.array(X_list), obs=obs_dict, dtype="int32")

    subset = adata[:2]
    assert subset.is_view
    # returns a copy of obs
    assert subset.obs.drop(columns=["oanno1"]).columns.tolist() == ["oanno2", "oanno3"]
    assert subset.is_view
    # would modify obs, so it should actualize subset and not modify adata
    subset.obs.drop(columns=["oanno1"], inplace=True)
    assert not subset.is_view
    assert subset.obs.columns.tolist() == ["oanno2", "oanno3"]

    assert adata.obs.columns.tolist() == ["oanno1", "oanno2", "oanno3"]
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