Errors when custom gradients are being used in TPU
@abhaskumarsinha Thank you for raising the issue. 
In order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported here. Thank you!! Hello @sushreebarsa Apologies for the late reply.

Thank you for looking into the issue. A sample reproducible code is given below:
```
import tensorflow as tf
import numpy as np
import os

tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.TPUStrategy(tpu)

# Generate random training data
num_samples = 1000
input_dim = 10
output_dim = 1

x_train = np.random.random((num_samples, input_dim))
y_train = np.random.random((num_samples, output_dim))

# Convert the training data into TensorFlow datasets
batch_size = 32
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size).repeat()

# Custom training loop
epochs = 10

with strategy.scope():
    # Define the model
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(32, activation='relu', input_shape=(input_dim,)),
        tf.keras.layers.Dense(output_dim)
    ])

    # Define the loss function
    loss_object = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)

    # Define the optimizer
    optimizer = tf.keras.optimizers.Adam()


    for epoch in range(epochs):
        for inputs, labels in train_dataset:
            with tf.GradientTape() as tape:
                predictions = model(inputs, training=True)
                loss = loss_object(labels, predictions)
            gradients = tape.gradient(loss, model.trainable_variables)


            # Create custom gradients

            grads = []
            for g in gradients:
              grads += [tf.ones((tf.shape(g)))]

            optimizer.apply_gradients(zip(grads, model.trainable_variables))
```

Here's a reproducible Colab Link: https://colab.research.google.com/drive/1yj7pGuFS-Ottf1eM9ua3a_cCf3VdoU0Z?usp=sharing

Please look into the issue and update us with any possible solution (even temporarily) to deal with the issue.

Thank You. @abhaskumarsinha I was able to replicate the issue in TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/c44e879509f73128a791c9957fe16306/60906.ipynb), [v2.13](https://colab.research.google.com/gist/sushreebarsa/c495b13f6eeb80c1408d7f96f94dbb91/60906.ipynb#scrollTo=AHBpLHNWX6kS) and faced **TypeError** in [tf-nightly](https://colab.research.google.com/gist/sushreebarsa/91e477da76917eba3668cf87a94ac49e/60906.ipynb#scrollTo=4DiN9Sz7X_KB). Please find the gist here. Could you please confirm the same? Thank you! Hello @sushreebarsa 

Yes. I think the issue here is common for 2.12/13. I'm not sure about tf-nightly (for some reason I can't use them on my device due to some custom configurations, so I can confirm the same thing on Google Colab) but those modules certainly need a check. Thank you. @abhaskumarsinha Thank you for the response!
@sachinprasadhs Could you please take a look at this issue?
Thank you!
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

v2.12.0-rc1-12-g0db597d0d75 2.12.0

### Custom Code

Yes

### 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 Behaviour?

When writing custom gradient modules in TensorFlow (not using tf.GradientTape()) and applying it using an existing optimizer causes errors. I don't see any visible difference in tf.GradientShape() gradients and custom ones.

### Standalone code to reproduce the issue

```shell
with strategy.scope():
  with tf.GradientTape() as tape:
    model = ... #using any model
    loss = ... #using any loss function here
    loss_n = loss(y_batch, model(x_batch))

  grads = tape.gradient(loss_n, model.trainable_weights)
  new_grads = []
  
  for g in grads:
    new_grads += [tf.ones((tf.shape(g)))]
  
  optimizer.apply_gradients(zip(new_grads, model.trainable_weights)) #error here
```
```


### Relevant log output

```shell
Should be similar to below:


AttributeError                            Traceback (most recent call last)
<ipython-input-25-c9f1f22a039f> in <cell line: 1>()
     50       grads = tape.gradient(l, model.trainable_weights)
     51       c = grads[0]
---> 52       opt.apply_gradients(zip(grads, model.trainable_weights))
     53       print('Loss this batch: ' + closure().numpy())
     54       opt.next_steps()

2 frames
/usr/local/lib/python3.10/dist-packages/keras/optimizers/optimizer.py in apply_gradients(self, grads_and_vars, name, skip_gradients_aggregation, **kwargs)
   1171         )
   1172         if not skip_gradients_aggregation and experimental_aggregate_gradients:
-> 1173             grads_and_vars = self.aggregate_gradients(grads_and_vars)
   1174         return super().apply_gradients(grads_and_vars, name=name)
   1175 

/usr/local/lib/python3.10/dist-packages/keras/optimizers/optimizer.py in aggregate_gradients(self, grads_and_vars)
   1137           List of (gradient, variable) pairs.
   1138         """
-> 1139         return optimizer_utils.all_reduce_sum_gradients(grads_and_vars)
   1140 
   1141     def apply_gradients(

/usr/local/lib/python3.10/dist-packages/keras/optimizers/utils.py in all_reduce_sum_gradients(grads_and_vars)
     40         else:
     41             # TODO(b/183257003): Remove this branch
---> 42             reduced = tf.distribute.get_replica_context().merge_call(
     43                 _all_reduce_sum_fn, args=(filtered_grads_and_vars,)
     44             )

AttributeError: 'NoneType' object has no attribute 'merge_call'
```
```
</details>