No gradients provided for any variable
Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62480">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62480">No</a>
 After carefully reading through other similar issues, I found out that if the final output layer or anywhere in the graph had any TF functions it won't calculate gradient. I had `tf.abs` and `tf.round` in my final layer which was preventing gradient calculation and throwing this error. After, removing them the model trains as expected
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When calling `fit` on sub-classed Layers and Model, it throws **ValueError: No gradients provided for any variable** with or without Gradient Tape context. I have also tried defining functions with sub-layers and constructing model using Model API i.e. not sub-classing it but I get the same error

### Standalone code to reproduce the issue

```shell
I'm putting a link to the question I have asked on Stackoverflow as it is quite a bit of code to reproduce the issue: https://stackoverflow.com/questions/77546477/how-to-build-a-model-and-train-it-with-tensorflow-keras-sub-classing
```


### Relevant log output

```shell
File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/engine/training.py", line 1401, in train_function  *
        return step_function(self, iterator)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/engine/training.py", line 1384, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/engine/training.py", line 1373, in run_step  **
        outputs = model.train_step(data)
    File "/root/.ipykernel/2759/command-461111845465809-3511162167", line 27, in train_step
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/optimizers/optimizer.py", line 1222, in apply_gradients
        grads_and_vars = self.aggregate_gradients(grads_and_vars)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/optimizers/optimizer.py", line 1184, in aggregate_gradients
        return optimizer_utils.all_reduce_sum_gradients(grads_and_vars)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/optimizers/utils.py", line 33, in all_reduce_sum_gradients
        filtered_grads_and_vars = filter_empty_gradients(grads_and_vars)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/keras/src/optimizers/utils.py", line 77, in filter_empty_gradients
        raise ValueError(

    ValueError: No gradients provided for any variable: (['trxster_7/encoder_10/en_embed_layer/embeddings:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/query/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/query/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/key/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/key/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/value/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/value/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/attention_output/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/en_mha_layer_1/attention_output/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/dense_283/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/dense_283/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/dense_284/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_136/dense_284/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_136/layer_normalization_340/gamma:0', 'trxster_7/encoder_10/encoder_sub_layer_136/layer_normalization_340/beta:0', 'trxster_7/encoder_10/encoder_sub_layer_136/layer_normalization_341/gamma:0', 'trxster_7/encoder_10/encoder_sub_layer_136/layer_normalization_341/beta:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/query/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/query/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/key/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/key/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/value/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/value/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/attention_output/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/en_mha_layer_2/attention_output/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/dense_285/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/dense_285/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/dense_286/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_137/dense_286/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_137/layer_normalization_342/gamma:0', 'trxster_7/encoder_10/encoder_sub_layer_137/layer_normalization_342/beta:0', 'trxster_7/encoder_10/encoder_sub_layer_137/layer_normalization_343/gamma:0', 'trxster_7/encoder_10/encoder_sub_layer_137/layer_normalization_343/beta:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/query/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/query/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/key/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/key/bias:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/value/kernel:0', 'trxster_7/encoder_10/encoder_sub_layer_138/en_mha_layer_3/value/bias:0', 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```
