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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def main():
 """Module main execution"""

    # Initialization variables - update to change your model and execution context
    model_prefix = "FCN8s_VGG16"
    epoch = 19

    # By default, MXNet will run on the CPU. Change to ctx = mx.gpu() to run on GPU.
    ctx = mx.cpu()

    fcnxs, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(model_prefix, epoch)
    fcnxs_args["data"] = mx.nd.array(get_data(args.input), ctx)
    data_shape = fcnxs_args["data"].shape
    label_shape = (1, data_shape[2]*data_shape[3])
    fcnxs_args["softmax_label"] = mx.nd.empty(label_shape, ctx)
    exector = fcnxs.bind(ctx, fcnxs_args, args_grad=None, grad_req="null", aux_states=fcnxs_args)
    exector.forward(is_train=False)
    output = exector.outputs[0]
    out_img = np.uint8(np.squeeze(output.asnumpy().argmax(axis=1)))
    out_img = Image.fromarray(out_img)
    out_img.putpalette(get_palette())
    out_img.save(args.output)