Op request: layer normalization
Bump ^ We are also interested in this. It appears that exporting a model that uses `LayerNormalization` will disable the TfLite XNNPack delegate, thus reducing performance of our model by a lot. 

Details:
After exporting the model, one of the layer norm tensors has `allocation_type==kTfLiteDynamic`. Since TfLiteXNNPackDelegate does not support dynamically allocated tensors (`delegate->flags & kTfLiteDelegateFlagsAllowDynamicTensors == 0`), the delegate is disabled and TF lite switches to using the default backend. I also would be very interested in this. While layer norms can be decomposed into multiple operators (`Mean`, `SquaredDifference`, `Rsqrt` etc..), this becomes problematic when doing quantisation (what operators should a quantised layer norm be converted into?)
Layer normalization is available in tensorflow https://www.tensorflow.org/api_docs/python/tf/keras/layers/LayerNormalization
It is not part of the tflite supported ops
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/builtin_ops.h

I would like to request support for this op. Please let me know if it is in development or scoped for future release.
