message: Doc formatting fixes
  Summary: Ref T16784
  Reviewers: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, davidn, georgep
  Maniphest Tasks: T16784
diff: @@ -13,7 +13,8 @@ function. Optimisation of the host embedding is described in the
  :py:class:`tensorflow.python.ipu.embedding_ops.HostEmbeddingOptimizerSpec`
  class, which currently only supports SGD with a constant learning rate.
  -Note that IPU host embeddings are not recommended for use in pipelines and will
  +.. note::
  + IPU host embeddings are not recommended for use in pipelines and will
  likely decrease the pipeline's parallel efficiency.
  Usage
  @@ -54,7 +55,7 @@ To overcome this constraint we offer two partitioning strategies. Each has
  tradeoffs which can be chosen depending on the application.
  Token Strategy
  -^^^^^^^^^^^^^^
  +--------------
  The token strategy unsurprisingly chooses to partition the embedding on the
  token axis. This means that there will be ``ceil(t/r)`` whole tokens on each
  replica, where ``t`` is the token count and ``r`` is the replica count.
  @@ -66,7 +67,7 @@ must introduce cross-replica operations to allow lookups and updates on the
  whole table. Below is the psuedo-code, with explicit types and static shapes,
  for how this is implemented:
  -.. code-block:: cpp
  +.. code-block:: none
  // Psuedo-code assuming we have table size `t`, and replica count `r`.
  f16[14, 64] global_lookup(
  local_table : f16[ceil(t/r), 64]
  @@ -100,7 +101,7 @@ for how this is implemented:
  return reshape(result), shape=[14, 64] : f16[14, 64]
  Encoding Strategy
  -^^^^^^^^^^^^^^^^^
  +-----------------
  The encoding strategy, in contrast to the token strategy, chooses to partition
  the embedding on the encoding axis. This means that there will be ``ceil(1/r)``
  of every tokens on each replica, where ``r`` is the replica count. This means
  @@ -114,7 +115,7 @@ table is private, so we must introduce cross-replica operations to allow lookups
  and updates on the whole table. Below is the psuedo-code, with explicit types
  and static shapes, for how this is implemented:
  -.. code-block:: cpp
  +.. code-block:: none
  // Psuedo-code assuming we have table size `t`, replica count `r`, and
  // encoding size `e`.
  f16[14, e] global_lookup(
  @@ -142,7 +143,7 @@ and static shapes, for how this is implemented:
  return slice(result), dim=1, begin=0, end=e : f16[14, e]
  Summary
  -^^^^^^^
  +-------
  Although it is application dependant, generally the token strategy is used when
  the encoding is much smaller than the token count. An example application for
  this would be language models where the vocabulary size is much larger than the
  