class_docstr: Modules recursively compose other Modules and parameters to create models.
  Compared to the `tf.Module` base class, `gpflow.Module` includes additional support for handling `gpflow.Parameter` attributes, see :py:attr:`~parameters` and :py:attr:`~trainable_parameters`.
  It also adds pretty-printing within IPython and Jupyter notebooks.
  All GPflow models, kernels, mean functions etc. are Modules.
  See `this guide <https://gpflow.github.io/GPflow/develop/notebooks/getting_started/parameters_and_their_optimisation.html#The-Module-and-Parameter-classes>`_
  for an introduction to this class.
  See also `TensorFlow's documentation <https://www.tensorflow.org/api_docs/python/tf/Module>`_ for the base class
  which goes into more detail as to why we use Module objects to compose things.
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docstr: Nice representation of GPflow objects in IPython/Jupyter notebooks 
docstr_tok: ["Nice","representation","of","GPflow","objects","in","IPython\/Jupyter","notebooks"]
code: def _repr_html_(self) -> str:
      """Nice representation of GPflow objects in IPython/Jupyter notebooks"""
      from html import escape
  
      return self._representation_table(escape(repr(self)), "html")
  
imports: from enum import Enum
  from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Tuple, Union
  import numpy
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes
  from typing_extensions import Final
  from .config import default_float, default_summary_fmt
  from .type_flags import GENERIC_NP_ARRAYS, NP_TYPE_CHECKING
label_desc: Use image node_id 4 for calling the Module obj's underlying member method code with example usage: obj._repr_html_() and returns: self