I am not sure if I understand the idea of tensorflow keras [mixed precision](https://www.tensorflow.org/guide/mixed_precision). My goal is to run a `tf.keras` model with floating point 16 precision to improve inference speed. Can this be done with mixed precision?

I am setting this policy before training my model:

```py
from tensorflow.keras.mixed_precision import experimental as mixed_precision
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

```

Or this is just to speed-up training. If this is the case, how could I achieve weights/activations of my tf.keras model to have FP16 precision?

*Note: I am using `tensorflow==2.3.0`*