def run_model(m, input, randomize):
    torch_glow.disableFusionPass()
    traced_m = torch.jit.trace(m, input)
    input_meta = InputMeta()
    input_meta.set_same_as(input)
    inputs = [input_meta]
    options = CompilationOptions()
    options.backend = 'Interpreter'
    options.randomize_constants = randomize
    spec = GlowCompileSpec()
    spec.set(inputs, options)
    glow_m = torch_glow.to_glow(traced_m, {'forward': spec})
    return glow_m.forward(input)
def run_model(m, input, randomize):
    torch_glow.disableFusionPass()
    traced_m = torch.jit.trace(m, input)
    input_meta = InputMeta()
    input_meta.set_same_as(input)
    inputs = [input_meta]
    options = CompilationOptions()
    options.backend = 'Interpreter'
    options.randomize_constants = randomize
    spec = GlowCompileSpec()
    spec.set(inputs, options)
    glow_m = torch_glow.to_glow(traced_m, {'forward': spec})
    return glow_m.forward(input)