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: :param latent_dim: number of arguments to the `conditional_distribution` callable
  :param conditional_distribution: function from F to a tfp Distribution,
      where F has shape [..., latent_dim]
docstr_tok: [":","param","latent_dim",":","number","of","arguments","to","the","`","conditional_distribution","`","callable",":","param","conditional_distribution",":","function","from","F","to","a","tfp","Distribution",",","where","F","has","shape","[","...",",","latent_dim","]"]
code: def __init__(
      self,
      latent_dim: int,
      conditional_distribution: Callable[
          ..., tfp.distributions.Distribution
      ],
      **kwargs: Any,
  ):
      """
      :param latent_dim: number of arguments to the `conditional_distribution` callable
      :param conditional_distribution: function from F to a tfp Distribution,
          where F has shape [..., latent_dim]
      """
      super().__init__(latent_dim, **kwargs)
      self.conditional_distribution = conditional_distribution
  
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 1 to create a new MultiLatentTFPConditional object from inherited base classes: MultiLatentLikelihood with example: obj = MultiLatentTFPConditional(latent_dim, conditional_distribution)