docstr: The conditional marginal variance of Y|X,F: [var(Y₁|X,F), ..., var(Yₖ|X,F)]
  where K = observation_dim
  
  :param X: input tensor
  :param F: function evaluation tensor
  :returns: variance
docstr_tok: ["The","conditional","marginal","variance","of","Y|X",",","F",":","[","var","(","Y\u2081|X",",","F",")",",","...",",","var","(","Y\u2096|X",",","F",")","]","where","K","=","observation_dim",":","param","X",":","input","tensor",":","param","F",":","function","evaluation","tensor",":","returns",":","variance"]
code: def conditional_variance(
      self, X: TensorType, F: TensorType
  ) -> tf.Tensor:
      """
      The conditional marginal variance of Y|X,F: [var(Y₁|X,F), ..., var(Yₖ|X,F)]
      where K = observation_dim
  
      :param X: input tensor
      :param F: function evaluation tensor
      :returns: variance
      """
      return self._conditional_variance(X, F)
  
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 Likelihood obj's underlying member method code with example usage: obj.conditional_variance(X, F) and returns: self