class_docstr: A Likelihood which assumes that a single dimensional observation is driven
  by multiple latent GPs.
  
  Note that this implementation does not allow for taking into account
  covariance between outputs.
class_docstr_tok: ["A","Likelihood","which","assumes","that","a","single","dimensional","observation","is","driven","by","multiple","latent","GPs",".","Note","that","this","implementation","does","not","allow","for","taking","into","account","covariance","between","outputs","."]
code: def __init__(self, latent_dim: int, **kwargs: Any) -> None:
      super().__init__(
          input_dim=None,
          latent_dim=latent_dim,
          observation_dim=1,
          **kwargs,
      )
  
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 MultiLatentLikelihood object from inherited base classes: QuadratureLikelihood with example: obj = MultiLatentLikelihood(latent_dim)