code: def _predict_mean_and_var(
      self, X: TensorType, Fmu: TensorType, Fvar: TensorType
  ) -> MeanAndVariance:
      mvs = [
          lik.predict_mean_and_var(X, Fmu, Fvar)
          for lik in self.likelihoods
      ]
      mu_list, var_list = zip(*mvs)
      mu = tf.concat(mu_list, axis=1)
      var = tf.concat(var_list, axis=1)
      return mu, var
  
imports: import abc
  from typing import Any, Callable, Iterable, Optional, Sequence, Union
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import MeanAndVariance, Module, TensorType
  from ..quadrature import GaussianQuadrature, NDiagGHQuadrature, ndiag_mc
label_desc: Use image node_id 6 for calling the SwitchedLikelihood obj's underlying member method code with example usage: obj._predict_mean_and_var(X, Fmu, Fvar) and returns: mu,  var