def METHOD_NAME():
    model = ConvModel(3, 6, 2)
    tracer = ColoTracer()
    # graph():
    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
    #     %conv_weight : [#users=1] = get_attr[target=conv.weight]
    #     %conv_bias : [#users=1] = get_attr[target=conv.bias]
    #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%x, %conv_weight), kwargs = {})
    #     %view : [#users=1] = call_method[target=view](args = (%conv_bias, [1, -1, 1, 1]), kwargs = {})
    #     %add : [#users=1] = call_function[target=operator.add](args = (%conv2d, %view), kwargs = {})
    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
    #     return mul
    graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 3, 64, 64).to('meta')})
    # def forward(self, x : torch.Tensor):
    #     conv_weight = self.conv.weight
    #     conv_bias = self.conv.bias
    #     conv2d = torch.conv2d(x, conv_weight);  x = conv_weight = None
    #     view = conv_bias.view([1, -1, 1, 1]);  conv_bias = None
    #     add = conv2d + view;  conv2d = view = None
    #     mul = add * 2;  add = None
    #     return mul
    gm = ColoGraphModule(model, graph)
    gm.recompile()
    node_list = list(graph.nodes)
    for node in node_list:
        if node.op == 'output':
            continue
        assert hasattr(node, '_meta_data')
    weight_node = node_list[1]
    bias_node = node_list[2]
    conv_node = node_list[3]
    view_node = node_list[4]
    add_node = node_list[5]
    assert weight_node._meta_data.shape == (6, 3, 2, 2)
    assert bias_node._meta_data.shape == (6,)
    assert conv_node._meta_data.shape == (4, 6, 63, 63)
    assert view_node._meta_data.shape == (6, 1, 1)
    assert add_node._meta_data.shape == (4, 6, 63, 63)