code: def Test():
      z = tf.compat.v1.get_variable(
          name="y",
          shape=(),
          initializer=tf.random_normal_initializer(),
          trainable=True,
      )
      table_initializer = tf.lookup.KeyValueTensorInitializer(
          keys=[1, 2, 3, 4],
          values=[5, 6, 7, 8],
          key_dtype=tf.int32,
          value_dtype=tf.float32,
      )
      table = tf.lookup.StaticHashTable(
          table_initializer, default_value=tf.constant(0.0)
      )
  
      x = tf.placeholder(tf.int32, shape=(), name="input")
      y = table.lookup(x)
      r = tf.add(y, z)
  
      tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(
          x
      )
      tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(
          r
      )
  
      return (
          {
              "key": (
                  tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
                      inputs={"x": tensor_info_x},
                      outputs={"r": tensor_info_r},
                      method_name="some_function",
                  )
              )
          },
          tf.tables_initializer(),
          None,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: Test() and returns: dict,  tf,  None