TPU v3-8 CrossReplicaSum_33 Error
What's even more weirder is that I'm defining the input sizes explicitly like so:

```py
Config.COMPUTED_BATCH_SIZE = 128

with strategy.scope():
    model = my_model()
    input_shapes = [
        [Config.COMPUTED_BATCH_SIZE, 192], 
        [Config.COMPUTED_BATCH_SIZE, 192], 
        [COMPUTED_CHANNELS, 105, 129, 100], 
        [COMPUTED_CHANNELS, 105, 129, 100], 
        [COMPUTED_CHANNELS, 105, 129, 100], 
        [Config.COMPUTED_BATCH_SIZE, 70], 
        [Config.COMPUTED_BATCH_SIZE, 320]
    ]
    model.build(input_shape=input_shapes)
```

~But the batch size shown in the error log is 256 in [70, 256] which I assume is the transposed tensor.~

Ignore this since it appears that Hidden Size * 2 is also 256. So it could be a transpose inside a Dense layer.

Edit:

I've tracked it down to this code:

```py
hidden_size = 128
self.descriptor_embedding = layers.Dense(
    hidden_size * 2, # 256
    activation='relu',
    input_shape=(Config.COMPUTED_BATCH_SIZE, 70)
)


learned_descriptors = tf.expand_dims(
    self.descriptor_embedding(descriptors),
    1
) # [BS, 1, HS * 2]
``` It seems to be an issue with `tf.expand_dims(x, axis=1)`. Any axis other than 0 will cause this error. `tf.transpose` also causes this error.  @vivekjoshy,
When I tried to execute the code by explicitly setting the size after you decode, using tf.reshape, the code was executed successfully. While with the other approach it was executed with the error. 

```python
tf.reshape(tf.image.decode_jpeg(image, channels = 3),[256,256, 3]), class_idx

```

Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/115211b9a4bbadbb30533d3eea1b773d/untitled1908.ipynb).

 Fixed by passing in explicit shapes everywhere. 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/68210">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/68210">No</a>

### Issue type

Support

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

No

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

_No response_

### 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?

I'm getting this error when I run model.fit

```txt
tensorflow/core/tpu/kernels/tpu_compilation_cache_external.cc:112] Asked to propagate a dynamic dimension from hlo transpose.3750@{}@0 to hlo %all-reduce.3755 = f32[<=70,256]{1,0} all-reduce(f32[<=70,256]{1,0} %transpose.3750), replica_groups={{0,1,2,3,4,5,6,7}}, to_apply=%sum.3751, metadata={op_type="CrossReplicaSum" op_name="CrossReplicaSum_33" source_file="dummy_file_name" source_line=10}, which is not implemented.

1013 tpu_program_group.cc:90] Check failed: xla_tpu_programs.size() > 0 (0 vs. 0) 
```

I have a very large code base, and am unable to reproduce. I was using AUC metric, but removed it as seen here https://github.com/tensorflow/tensorflow/issues/33890. The issue still persists. 

Another relevant issue is https://github.com/tensorflow/tensorflow/issues/41590.

I have attached an MVCE example to reproduce, but it's still not enough to know that explicit shapes are needed as explained in #41590 since I don't know where I'm using transpose. I need a stack trace to point to where in the code it's causing the issue.

### Standalone code to reproduce the issue

```shell
https://colab.research.google.com/drive/1bYuuwG0pFnIQe1X6jA7FJMLjZtvAmqlj?usp=sharing
```


### Relevant log output

_No response_