def METHOD_NAME(inshape, kernel, pad,
                                                     stride, dilation, divisor,
                                                     with_bias, seed, ctx,
                                                     func_name):
    base_axis = len(inshape) - 2
    sample_channels = inshape[base_axis]
    outmap_channels = sample_channels // divisor
    rng = np.random.RandomState(seed)
    x = rng.randn(*inshape).astype(np.float32)
    w = rng.randn(*((sample_channels,) + kernel)).astype(np.float32)
    b = rng.randn(outmap_channels).astype(np.float32) if with_bias else None
    inputs = [x, w, b]
    func_args = [base_axis, pad, stride, dilation, divisor]
    reference = ref_depthwise_deconvolution_1d
    function_tester(rng, F.depthwise_deconvolution, reference, inputs,
                    func_args, atol_f=1e-4, atol_b=3e-3, dstep=1e-2,
                    ctx=ctx, func_name=func_name)