Repository: tensorflow
Issue: Create a trainable tensorflow or LiteRT (with signatures) graph from a frozen tensorflow model
Labels: stat:awaiting tensorflower,type:support,comp:lite,2.17

### Issue type

Support

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

Yes

### Source

binary

### TensorFlow version

tf 2.17

### Custom code

No

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I am working with a frozen TensorFlow model (saved as a .pb file) and am exploring ways to make it trainable again, in its original TensorFlow format and eventually a LiteRT model with [signatures to train](https://ai.google.dev/edge/litert/models/ondevice_training).

The goal is to restore training capabilities (such as fine-tuning or continued training) from a model that has already been frozen. I would appreciate guidance on how to approach this scenario.

### Key Points:
#### Frozen TensorFlow Model:
I have a TensorFlow model that was previously trained and saved in its frozen state (as a .pb file). This model no longer contains trainable variables, as they have been converted into constants. However, I would like to continue training this model on new data or fine-tune it for a different task.

**Question 1**: What is the recommended approach to restore a frozen TensorFlow model to a state where it can be trained or fine-tuned again? Specifically, how do I extract the original model architecture and variables from the frozen graph to make it trainable once more?

**Question 2**: If the frozen model has layers that are not needed for retraining, how can I unfreeze and selectively fine-tune only some parts of the model while keeping others frozen?

#### LiteRT Model:
I am also exploring the possibility of taking a LiteRT model that was previously trained & converted to LiteRT, and then restoring its training capability. 

**Question 3**: Is it possible to convert a TensorFlow Lite model back into TensorFlow (with the appropriate weights) and add appropriate training signatures and then convert it back to a trainable LiteRT model? If so, what are the best practices, and any potential challenges involved in such a conversion?

**Question 4**: If not, and I want to fine-tune a model that has been converted to LiteRT, would it be better to work with the original TensorFlow model and re-convert it to LiteRT after training, rather than trying to restore training from the LiteRT version?

#### Expected Outcome:
Any guidance on the best approach to make a frozen model trainable again (whether in TensorFlow or TensorFlow Lite) would be greatly appreciated. Specifically, I’m seeking clarity on:

- How to restore training capability from a frozen .pb file in TensorFlow.
- How to manage layer freezing or unfreezing during fine-tuning.
- Whether a TensorFlow Lite model can be retrained, or if it's more efficient to work with the TensorFlow version instead.

### Standalone code to reproduce the issue

```shell
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```


### Relevant log output

_No response_