.. blogpost::
    :title: onnxruntime shape [] != None
    :keywords: onnxruntime
    :date: 2021-08-10
    :categories: onnx

    `None` is the undefined shape, `[]` is an empty shape.
    And when shapes do not fit the results, the outputs can
    be suprising. The following example shows what :epkg:`onnxruntime`
    produces for the same graph except input and output shapes
    when defined as `None` and `[]`.

    .. runpython::
        :showcode:

        import numpy
        from onnx import helper, TensorProto
        from onnxruntime import InferenceSession

        def model(shape):
            X = helper.make_tensor_value_info('X', TensorProto.FLOAT, shape)
            Z = helper.make_tensor_value_info('Z', TensorProto.INT64, shape)
            node_def = helper.make_node('Shape', ['X'], ['Z'], name='Zt')
            graph_def = helper.make_graph([node_def], 'test-model', [X], [Z])
            model_def = helper.make_model(
                graph_def, producer_name='mlprodict', ir_version=7, producer_version='0.1',
                opset_imports=[helper.make_operatorsetid('', 13)])
            sess = InferenceSession(model_def.SerializeToString())
            rnd = numpy.random.randn(12).astype(numpy.float32)
            print("shape=%r results=%r" % (shape, sess.run(None, {"X": rnd})))

        model(None)
        model([])