def test_double_index(subset_func, subset_func2):
    adata = gen_adata((10, 10))
    obs_subset = subset_func(adata.obs_names)
    var_subset = subset_func2(adata.var_names)
    v1 = adata[obs_subset, var_subset]
    v2 = adata[obs_subset, :][:, var_subset]

    assert np.all(asarray(v1.X) == asarray(v2.X))
    assert np.all(v1.obs == v2.obs)
    assert np.all(v1.var == v2.var)
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