code: def _predict_mean_and_var(
      self, X: TensorType, Fmu: TensorType, Fvar: TensorType
  ) -> MeanAndVariance:
      if self.invlink is inv_probit:
          p = inv_probit(Fmu / tf.sqrt(1 + Fvar))
          return p, p - tf.square(p)
      else:
          # for other invlink, use quadrature
          return super()._predict_mean_and_var(X, Fmu, Fvar)
  
imports: from typing import Any, Callable
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..None import logdensities
  from ..base import AnyNDArray, MeanAndVariance, Parameter, TensorType
  from ..config import default_float
  from ..utilities import positive, to_default_int
  from .base import ScalarLikelihood
  from .utils import inv_probit
label_desc: Use image node_id 3 for calling the Bernoulli obj's underlying member method code with example usage: obj._predict_mean_and_var(X, Fmu, Fvar) and returns: p,  unknown, super