def METHOD_NAME(seed, base_axis, weight_shape, num_bits, bias,
                                  inq_iterations, selection_algorithm, func_seed, ctx, func_name):
    from nbla_test_utils import recomputation_test
    rng = np.random.RandomState(seed)
    # Input
    inputs = [rng.randn(2, 3, 4).astype(np.float32)]
    # Weights
    inputs += [rng.randn(*weight_shape).astype(np.float32)]
    # Indices
    inputs += [np.random.randint(2, size=weight_shape)]
    # Bias
    if bias:
        inputs += [rng.randn(*weight_shape[1:]).astype(np.float32)]
    else:
        inputs += [None]
    func_args = [base_axis, num_bits,
                 inq_iterations, selection_algorithm, func_seed]
    vinputs = []
    for i in inputs:
        if i is None:
            vinputs.append(None)
        else:
            vinputs.append(nn.Variable.from_numpy_array(i))
    recomputation_test(rng=rng, func=F.inq_affine, vinputs=vinputs,
                       func_args=func_args, func_kwargs={}, ctx=ctx)