message: Update docs in control_flow_ops.py for consistent Markdown rendering
diff: @@ -51,8 +51,8 @@ def for_loop(loop_fn, loop_fn_dtypes, iters, parallel_iterations=None):
  loop_fn: A function that takes an int32 scalar tf.Tensor object representing
  the iteration number, and returns a possibly nested structure of tensor
  objects. The shape of these outputs should not depend on the input.
  - loop_fn_dtypes: dtypes for the outputs of loop_fn.
  - iters: Number of iterations for which to run loop_fn.
  + loop_fn_dtypes: dtypes for the outputs of `loop_fn`.
  + iters: Number of iterations for which to run `loop_fn`.
  parallel_iterations: The number of iterations that can be dispatched in
  parallel. This knob can be used to control the total memory usage.
  @@ -137,7 +137,7 @@ def pfor(loop_fn, iters, fallback_to_while_loop=True, parallel_iterations=None):
  `pfor` has functionality similar to `for_loop`, i.e. running `loop_fn` `iters`
  times, with input from 0 to `iters - 1`, and stacking corresponding output of
  - each iteration. However the implementation does not use a tf.while_loop.
  + each iteration. However the implementation does not use a `tf.while_loop`.
  Instead it adds new operations to the graph that collectively compute the same
  value as what running `loop_fn` in a loop would compute.
  @@ -152,7 +152,7 @@ def pfor(loop_fn, iters, fallback_to_while_loop=True, parallel_iterations=None):
  reads, etc).
  - Conversion works only on a limited set of kernels for which a converter
  has been registered.
  - - loop_fn has limited support for control flow operations. tf.cond in
  + - `loop_fn` has limited support for control flow operations. `tf.cond` in
  particular is not supported.
  - `loop_fn` should return nested structure of Tensors or Operations. However
  if an Operation is returned, it should have zero outputs.
  @@ -166,9 +166,9 @@ def pfor(loop_fn, iters, fallback_to_while_loop=True, parallel_iterations=None):
  or Operation objects. Note that if setting `parallel_iterations` argument
  to something other than None, `loop_fn` may be called more than once
  during graph construction. So it may need to avoid mutating global state.
  - iters: Number of iterations for which to run loop_fn.
  + iters: Number of iterations for which to run `loop_fn`.
  fallback_to_while_loop: If true, on failing to vectorize an operation, pfor
  - fallbacks to using a tf.while_loop to dispatch the iterations.
  + fallbacks to using a `tf.while_loop` to dispatch the iterations.
  parallel_iterations: A knob to control how many iterations are vectorized
  and dispatched in parallel. The default value of None corresponds to
  vectorizing all the iterations. If `parallel_iterations` is smaller than
  @@ -337,7 +337,7 @@ def vectorized_map(fn, elems, fallback_to_while_loop=True):
  """Parallel map on the list of tensors unpacked from `elems` on dimension 0.
  - This method works similar to tf.map_fn but is optimized to run much faster,
  + This method works similar to `tf.map_fn` but is optimized to run much faster,
  possibly with a much larger memory footprint. The speedups are obtained by
  vectorization (see https://arxiv.org/pdf/1903.04243.pdf). The idea behind
  vectorization is to semantically launch all the invocations of `fn` in
  