code: def test_mean_functions_A_minus_A_equals_zero(
      mean_functions: Sequence[MeanFunction],
  ) -> None:
      """
      Tests that the addition the inverse of a mean function to itself is equivalent to having a
      Zero mean function: A + (-A) = 0
      """
      X, Y = rng.randn(Datum.N, Datum.input_dim), rng.randn(
          Datum.N, Datum.output_dim
      )
      Xtest = rng.randn(30, Datum.input_dim)
      A, A_inverse = mean_functions[0], mean_functions[-1]
      lhs = Additive(A, A_inverse)  # A + (-A)
      rhs = Zero()  # 0
  
      model_lhs = _create_GPR_model_with_bias(X, Y, mean_function=lhs)
      model_rhs = _create_GPR_model_with_bias(X, Y, mean_function=rhs)
  
      mu_lhs, var_lhs = model_lhs.predict_f(Xtest)
      mu_rhs, var_rhs = model_rhs.predict_f(Xtest)
  
      assert_allclose(mu_lhs, mu_rhs)
      assert_allclose(var_lhs, var_rhs)
  
imports: from typing import Any, Sequence, Tuple, Type
  import numpy
  import pytest
  import tensorflow_probability
  from check_shapes import check_shapes
  from numpy.testing import assert_allclose
  import gpflow
  from gpflow.base import AnyNDArray, TensorType
  from gpflow.config import default_int
  from gpflow.functions import Additive, Constant, Linear, MeanFunction, Polynomial, Product, SwitchedMeanFunction, Zero
  from gpflow.inducing_variables import InducingPoints
label_desc: Use image node_id 5 for calling a global function with example usage: test_mean_functions_A_minus_A_equals_zero(mean_functions) without return types