class_docstr: The soft-max multi-class likelihood.  It can only provide a stochastic
  Monte-Carlo estimate of the variational expectations term, but this
  added variance tends to be small compared to that due to mini-batching
  (when using the SVGP model).
class_docstr_tok: ["The","soft-max","multi-class","likelihood",".","It","can","only","provide","a","stochastic","Monte-Carlo","estimate","of","the","variational","expectations","term",",","but","this","added","variance","tends","to","be","small","compared","to","that","due","to","mini-batching","(","when","using","the","SVGP","model",")","."]
code: def _conditional_variance(
      self, X: TensorType, F: TensorType
  ) -> tf.Tensor:
      p = self.conditional_mean(X, F)
      return p - p**2
  
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 Softmax obj's underlying member method code with example usage: obj._conditional_variance(X, F) and returns: unknown