class_docstr: MultiLatent likelihood where the conditional distribution
  is given by a TensorFlow Probability Distribution.
class_docstr_tok: ["MultiLatent","likelihood","where","the","conditional","distribution","is","given","by","a","TensorFlow","Probability","Distribution","."]
docstr: The conditional marginal variance of Y|F: [Var(Y₁|F)]
  
  :param F: function evaluation Tensor, with shape [..., latent_dim]
  :returns: variance [..., 1]
docstr_tok: ["The","conditional","marginal","variance","of","Y|F",":","[","Var","(","Y\u2081|F",")","]",":","param","F",":","function","evaluation","Tensor",",","with","shape","[","...",",","latent_dim","]",":","returns",":","variance","[","...",",","1","]"]
code: def _conditional_variance(
      self, X: TensorType, F: TensorType
  ) -> tf.Tensor:
      """
      The conditional marginal variance of Y|F: [Var(Y₁|F)]
  
      :param F: function evaluation Tensor, with shape [..., latent_dim]
      :returns: variance [..., 1]
      """
      return self.conditional_distribution(F).variance()
  
file_path: GPflow/gpflow/likelihoods/multilatent.py
imports: from typing import Any, Callable, Optional, Type
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import TensorType
  from ..utilities import positive
  from .base import QuadratureLikelihood
label_desc: Use image node_id 4 for calling the MultiLatentTFPConditional obj's underlying member method code with example usage: obj._conditional_variance(X, F) and returns: self