class_docstr: The Gaussian likelihood is appropriate where uncertainties associated with
  the data are believed to follow a normal distribution, with constant
  variance.
  
  Very small uncertainties can lead to numerical instability during the
  optimization process. A lower bound of 1e-6 is therefore imposed on the
  likelihood variance by default.
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code: def _conditional_variance(
      self, X: TensorType, F: TensorType
  ) -> tf.Tensor:
      shape = tf.shape(F)
      return tf.broadcast_to(self._variance(X), shape)
  
imports: from math import sqrt
  from typing import Any, Callable, Optional
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..None import logdensities
  from ..base import MeanAndVariance, TensorType
  from ..config import default_likelihood_positive_minimum
  from ..utilities.parameter_or_function import ConstantOrFunction, ParameterOrFunction, evaluate_parameter_or_function, prepare_parameter_or_function
  from .base import ScalarLikelihood
  from .utils import inv_probit
label_desc: Use image node_id 6 for calling the Gaussian obj's underlying member method code with example usage: obj._conditional_variance(X, F) and returns: tf