LSTM not quantized weights after model_save_quantized_weights

I'm trying to quantize LSTM network from the notebook you have: https://github.com/google/qkeras/blob/eb6e0dc86c43128c6708988d9cb54d1e106685a4/notebook/QRNNTutorial.ipynb.
After seeing [this issue](https://github.com/google/qkeras/issues/60) I've changed the config file to look like this:
```
bits = 8
quantizer_config = {
"bidirectional": {
'activation' : f"quantized_tanh({bits}, 0, alpha=1)",
'recurrent_activation' : f"quantized_relu({bits}, 0, alpha=1)",
'kernel_quantizer' : f"quantized_bits({bits}, 0, alpha=1)",
'recurrent_quantizer' : f"quantized_bits({bits}, 0, alpha=1)",
'bias_quantizer' : f"quantized_bits({bits}, 0, alpha=1)",
},
"dense": {
'kernel_quantizer' : f"quantized_bits({bits}, 0, alpha=1)",
'bias_quantizer' : f"quantized_bits({bits}, 0, alpha=1)",
},
"embedding_act": f"quantized_bits({bits}, 0, alpha=1)",
}
```
I'm training this model and I apply model_save_quantized_weights function. Then when I print weights, they are still in floating point:
```
model_save_quantized_weights(qmodel, "quant_weights.h5")
for layer in qmodel.layers:
for i, weights in enumerate(layer.get_weights()):
print(weights)
```
The example of printed weights:
```
[[ 0.08662941 -0.05719738 -0.05291974 ... -0.6543944 0.13776235
0.39616233]
[ 0.1125139 -0.09429312 0.16143066 ... 0.12786183 0.1350617
-0.02886106]
[ 0.14597955 0.11171963 0.14480615 ... 0.31972137 0.17480904
-0.15030576]
...
[ 0.03954179 -0.01506722 -0.09103195 ... -0.11322258 0.07701313
-0.12551346]
[-0.02650027 0.0823105 -0.01624984 ... 0.2262283 0.08772285
-0.17474762]
[-0.11531919 -0.02932754 0.1707585 ... 0.18108878 0.03475188
-0.16486846]]

```
Could you, please, guide me, what should I do get int8 weights?