code: def _predict_mean_and_var(
      self, X: TensorType, Fmu: TensorType, Fvar: TensorType
  ) -> MeanAndVariance:
      possible_outputs = [
          tf.fill(
              tf.stack([tf.shape(Fmu)[0], 1]),
              np.array(i, dtype=np.int64),
          )
          for i in range(self.num_classes)
      ]
      ps = [
          self._predict_non_logged_density(X, Fmu, Fvar, po)
          for po in possible_outputs
      ]
      ps = tf.transpose(tf.stack([tf.reshape(p, (-1,)) for p in ps]))
      return ps, ps - tf.square(ps)
  
imports: from typing import Any, Optional
  import numpy
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import MeanAndVariance, Module, Parameter, TensorType
  from ..config import default_float
  from ..quadrature import hermgauss
  from ..utilities import to_default_float, to_default_int
  from .base import Likelihood, MonteCarloLikelihood
label_desc: Use image node_id 4 for calling the MultiClass obj's underlying member method code with example usage: obj._predict_mean_and_var(X, Fmu, Fvar) and returns: ps,  unknown