din = va.array( np.random.randint(-2**30, 2**30 - 1, size=(1000,)), dtype=va.int32, ) dout = graph.run(din) # Complex input re = np.random.randint(-2**12, 2**12 - 1, size=(1024,), dtype=np.int16) im = np.random.randint(-2**12, 2**12 - 1, size=(1024,), dtype=np.int16) cin = va.array(re + 1j * im, dtype=va.cint16) cout = graph.run(cin) # Reshape outputs out = graph.run(din, shape=(32, 32)) out1, out2 = graph.run(in1, in2, shape=[(256,), (128, 2)]) Input-format rules, inspection helpers (getInputSpec, getOutputSpec), and 2-D column-to-port mapping are identical to hlsKernel.