def test_fp16_input_dtype_fp32_precision(self, float32_input_model_add_op, float32_input_model_relu_ops,
                                             int32_input_model):
        """
        Same test as test_fp16_input_dtype, but with Float32 precision
        """
        mlmodel = ct.convert(
            float32_input_model_add_op,
            inputs=[ct.TensorType(dtype=np.float16)],
            outputs=[ct.TensorType(dtype=np.float32)],
            minimum_deployment_target=ct.target.macOS13,
            compute_precision=ct.precision.FLOAT32,
        )
        assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "add"])
        assert_input_dtype(mlmodel, expected_type_str="fp16")
        assert_output_dtype(mlmodel, expected_type_str="fp32")
        verify_prediction(mlmodel)

        mlmodel = ct.convert(
            float32_input_model_relu_ops,
            inputs=[ct.TensorType(dtype=np.float16)],
            outputs=[ct.TensorType(dtype=np.float32)],
            minimum_deployment_target=ct.target.macOS13,
            compute_precision=ct.precision.FLOAT32,
        )
        assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "relu"])
        assert_input_dtype(mlmodel, expected_type_str="fp16")
        assert_output_dtype(mlmodel, expected_type_str="fp32")

        Same test as test_fp16_input_dtype, but with Float32 precision
        
coremltools/converters/mil/frontend/tensorflow/test/test_tf_conversion_api.py