Hongyu, Chiu

Some additional information: |backend|dtype of `arange(dtype=None)`|can perform `sqrt`?|dtype after `sqrt`| |-|-|-|-| |numpy|int64|yes|float64| |jax|int32|yes|float32| |tensorflow|int32|no|X| |torch|float32|yes|float32| I think we should standardize the behavior of these two ops?

Hi @sampathweb Yes, we can explicitly add them using `metrics`. However, this approach might be less efficient when the loss computation is expensive. It is also inconvenient to require the...

It is common to use `tf.keras.layers.Multiply` but it lacks support from tfmot. [https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantize_registry.py#L162](https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantize_registry.py#L162) Does it has any potential risk for adding support of Multiply? I can surpass the error and...

I'm willing to contribute but I can't access the google docs. Is this contributions-welcome?

> @james77777778 thanks for the offer. Can you explain the source of the speedups? @jbischof Sure! I pick some layers for clarification: |Layer|Why|Speedup| |-|-|-| |RandomCropAndResize|utilizes `tf.image.crop_and_resize` with vectorized design|+1150%| |Resize...