converting LSTM layer to tflite with float16 fails
@sronen71 For LSTM conversion, this is an [article](https://www.tensorflow.org/lite/convert/rnn) and colab describing the process, and you can follow the [colab gist](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/experimental_new_converter/Keras_LSTM_fusion_Codelab.ipynb) to do it. For [4 kinds of quantizations](https://www.tensorflow.org/lite/performance/model_optimization), maybe you can also try other ways like dynamic range. Hope it could help? Please let us know. Thank you! @sushreebarsa 
I checked that information and colab example previously, and based my colab gist on that.
I find the original colab example is broken right now, because of tf-nightly error.
I was able to run it yesterday successfully, however, it only uses the vanilla convert.
When I try to use the optimize option with float16 target, as in the colab gist I attached, the reported defect is observed. @pkgoogle I was able to reproduce this issue. Please find this [gist](https://colab.research.google.com/gist/pjpratik/f814b84dc79edfee54977c82c98cc34c/61370.ipynb).

Could you please look into this?

Thanks. I was able to replicate with @pjpratik's exact same gist, @abattery, can you please take a look? Thanks. Hi, can i work on this issue ? Hi @devadigapratham, we always welcome any contribution from the community. Please let us know if you have any questions. Also running into this issue for float16 tflite conversions
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

Colab

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Tflite converter fails to convert model with LSTM layer to float16 target.
It runs for a very long time, increases RAM consumption to the maximum system available, then crashes.

Expected behavior: should convert.

Workaround: instead of LSTM layer, use  RNN wrapper of LSTMCell instead of the LSTM layer. 
Tflite is successful in converting the alternative with float16 target.


### Standalone code to reproduce the issue

```shell
See this gist:
https://colab.research.google.com/gist/sronen71/9b016245f507280f867841a7161fad8d/keras-lstm-fusion-codelab.ipynb
```


### Relevant log output

```shell
Program crash after it is out of RAM.
```
