How to use the einops.asnumpy function in einops

model2.load_parameters(fp.name)

        assert numpy.allclose(asnumpy(model1(x)), asnumpy(model2(x)))

        # testing with symbolic (NB with fixed dimensions!)
        input = mxnet.sym.Variable('data', shape=x.shape)
        json = model1(input).tojson()
        model3 = mxnet.gluon.SymbolBlock(outputs=mxnet.sym.load_json(json), inputs=input)
        model4 = mxnet.gluon.SymbolBlock(outputs=mxnet.sym.load_json(json), inputs=input)
        model3.initialize(ctx=mxnet.cpu())
        model3(x)

        with tempfile.NamedTemporaryFile(mode='r+b') as fp:
            model3.save_parameters(fp.name)
            model4.load_parameters(fp.name)
        assert numpy.allclose(asnumpy(model3(x)), asnumpy(model4(x)))

        try:
            # hybridization doesn't work
            model1.hybridize(static_alloc=True, static_shape=True)
            model1(x)
        except:
            pass

]
            for layer in layers:
                model.add(layer)
            model.initialize(mxnet.init.Xavier(), ctx=mxnet.cpu())
            return model

        model1 = create_model()
        model2 = create_model()
        x = mxnet.ndarray.random_normal(shape=[10, 3, 32, 32])
        assert not numpy.allclose(asnumpy(model1(x)), asnumpy(model2(x)))

        with tempfile.NamedTemporaryFile(mode='r+b') as fp:
            model1.save_parameters(fp.name)
            model2.load_parameters(fp.name)

        assert numpy.allclose(asnumpy(model1(x)), asnumpy(model2(x)))

        # testing with symbolic (NB with fixed dimensions!)
        input = mxnet.sym.Variable('data', shape=x.shape)
        json = model1(input).tojson()
        model3 = mxnet.gluon.SymbolBlock(outputs=mxnet.sym.load_json(json), inputs=input)
        model4 = mxnet.gluon.SymbolBlock(outputs=mxnet.sym.load_json(json), inputs=input)
        model3.initialize(ctx=mxnet.cpu())
        model3(x)

        with tempfile.NamedTemporaryFile(mode='r+b') as fp:
            model3.save_parameters(fp.name)
            model4.load_parameters(fp.name)
        assert numpy.allclose(asnumpy(model3(x)), asnumpy(model4(x)))

        try:
            # hybridization doesn't work