The Whisper Hybrid encoder model with dynamic quantization functions properly, but it fails when using a full int8 model with post-training quantization.

Original Issue: https://github.com/tensorflow/tensorflow/issues/59716 Opening on behalf of @nyadla-sys

1. System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
TensorFlow installation (pip package or built from source):
TensorFlow library (version, if pip package or github SHA, if built from source):

2. Code

Refer the below colab to reproduce the issue .. https://colab.research.google.com/drive/1S_3bVlwRZkMaYvvKwtPfWlyXQLS0Bvxa?usp=sharing

(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.

Option B: Paste your code here or provide a link to a custom end-to-end colab

(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.

3. Failure after conversion

If the conversion is successful, but the generated model is wrong, then state what is wrong:

Model produces wrong results and/or has lesser accuracy.
Model produces correct results, but it is slower than expected.

4. (optional) RNN conversion support

If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

5. (optional) Any other info / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

google-ai-edge / LiteRT

The Whisper Hybrid encoder model with dynamic quantization functions properly, but it fails when using a full int8 model with post-training quantization. #136