code: def Test():
      x = tf.constant([[1.0], [1.0], [1.0]])
      y = tf.get_variable(
          name="y",
          shape=(1, 3),
          initializer=tf.random_normal_initializer(),
          trainable=True,
      )
      r = tf.matmul(x, y)
  
      tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
      tensor_info_r = tf.saved_model.utils.build_tensor_info(r)
  
      signature_def = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs={"x": tensor_info_x},
              outputs={"r": tensor_info_r},
              method_name="some_function",
          )
      )
      signature_def2 = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs={"x": tensor_info_x},
              outputs={"r": tensor_info_r},
              method_name="some_other_function",
          )
      )
  
      # Create two signatures that share the same variable.
      return {"key": signature_def, "key2": signature_def2}, None, None
  
label_desc: Use image node_id 1 for calling a global function with example usage: Test() and returns: dict,  None,  None