def test_input_dynamic_without_inputs_param(self, backend, compute_unit):
        """The `inputs` param is not provided for a dynamic input (shape has `None`)."""
        with tf.Graph().as_default() as graph:
            x = tf.placeholder(tf.float32, shape=(None, None, 3), name="input")
            x1 = tf.placeholder(tf.float32, shape=(1, 2, 3), name="input_1")
            y = tf.nn.relu(x, name="output")
            y1 = tf.nn.relu(x1, name="output_1")

        convert_to = backend[0]
        if convert_to == "mlprogram":
            with pytest.warns(
                UserWarning,
                match="Some dimensions in the input shape are unknown, hence they are set to "
                "flexible ranges with lower bound and default value = 1, and upper bound = 2. "
            ):
                mlmodel = ct.convert(
                    graph,
                    convert_to=convert_to,
                    compute_units=compute_unit,
                )
        else:
            mlmodel = ct.convert(
                graph,
                convert_to=convert_to,
                compute_units=compute_unit,
            )

        spec = mlmodel.get_spec()
        assert list(spec.description.input[0].type.multiArrayType.shape) == [1, 1, 3]
        assert (
            spec.description.input[0].type.multiArrayType.shapeRange.sizeRanges[1].lowerBound == 1
        )
        assert (
            spec.description.input[0].type.multiArrayType.shapeRange.sizeRanges[1].upperBound == -1
            if convert_to == "neuralnetwork"
            else 2
        )
The `inputs` param is not provided for a dynamic input (shape has `None`).
coremltools/converters/mil/frontend/tensorflow/test/test_tf_conversion_api.py