code: def test_robust_max_multiclass_predict_log_density(
      num_classes: int,
      num_points: int,
      mock_prob: float,
      expected_prediction: float,
      tol: float,
      epsilon: float,
  ) -> None:
      class MockRobustMax(RobustMax):
          @inherit_check_shapes
          def prob_is_largest(
              self,
              Y: TensorType,
              mu: TensorType,
              var: TensorType,
              gh_x: TensorType,
              gh_w: TensorType,
          ) -> tf.Tensor:
              return (
                  tf.ones((num_points, 1), dtype=default_float())
                  * mock_prob
              )
  
      likelihood = MultiClass(
          num_classes, invlink=MockRobustMax(num_classes, epsilon)
      )
      X = tf.ones((num_points, 2))
      F = tf.ones((num_points, num_classes))
      rng = np.random.RandomState(1)
      Y = to_default_int(rng.randint(num_classes, size=(num_points, 1)))
      prediction = likelihood.predict_log_density(X, F, F, Y)
  
      assert_allclose(prediction, expected_prediction, tol, tol)
  
file_path: GPflow/tests/gpflow/likelihoods/test_multiclass.py
imports: import numpy
  import pytest
  import tensorflow
  from check_shapes import inherit_check_shapes
  from numpy.testing import assert_allclose
  from gpflow.base import AnyNDArray, TensorType
  from gpflow.config import default_float, default_int
  from gpflow.likelihoods import Bernoulli, MultiClass, RobustMax, Softmax
  from gpflow.utilities import to_default_float, to_default_int
label_desc: Use image node_id 4 for calling a global function with example usage: test_robust_max_multiclass_predict_log_density(num_classes, num_points, mock_prob, expected_prediction, tol, epsilon) and returns: unknown