message: [Docs] Document some known TF/XLA limitations.
diff: @@ -174,9 +174,33 @@ When filing bugs, attach the contents of the `/tmp/generated` directory
  If possible, try to isolate
  a bug to a single XLA program by using the
  -[`replay_computation`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/replay_computation.cc)
  +[`replay_computation`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/run_hlo_module_main.cc)
  and iteratively running it on generated programs.
  +## Known Issues
  +
  +Compilation with XLA can greatly improve the performance of your programs, but
  +the TensorFlow interop has a number of known sharp corners.
  +
  +### TensorArray TF/XLA Interconversion
  +
  +The problem manifests itself as an error message
  +`Support for TensorList crossing the XLA/TF boundary is not implemented`.
  +
  +XLA supports `tf.TensorArray`. However, the _interconversion_ between TF and
  +XLA representations is not implemented yet.
  +This error often arises when the `TensorArray` is used inside the compiled
  +block, but the derivative is taken outside.
  +
  +Workaround: compile the outermost scope which is taking the derivative.
  +
  +### Random Number Generation
  +
  +XLA currently ignores TF seeds to random operations. This affects stateful TF
  +random operations, such as `tf.random.normal`, or `tf.nn.dropout`. XLA will
  +behave as if the compilation was seeded with a new unique seed at each run. This
  +limitation does not apply to stateless random ops.
  +
  ## XLA Frontends
  Apart from TensorFlow, XLA programs can be generated by:
  