wrong calculations with TPU distribution strategy
@mohammad0081,
Could you please, share colab link or simple standalone code with supporting files to reproduce the issue in our environment. It helps us in localizing the issue faster. Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. yes , this issue will accur even with mnist dataset using tpu strategy, accuracy will be random ! 
about 10%

i will provide the code soon import tensorflow as tf
(x_train, y_train), (_, __) = tf.keras.datasets.mnist.load_data()
resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)

print("All devices: ", tf.config.list_logical_devices('TPU'))

def create_model():
  model = tf.keras.models.Sequential()
  import tensorflow.keras.layers as l
  model.add(l.Input(shape=(28, 28, 1)))
  model.add(l.Conv2D(64, (3, 3), activation = 'relu'))
  model.add(l.Conv2D(32, (3, 3), activation = 'relu'))
  model.add(l.Flatten())
  model.add(l.Dense(512, activation = 'relu'))
  model.add(l.Dense(64, activation = 'relu'))
  model.add(l.Dense(10, activation = 'softmax'))

  model.compile(optimizer = 'sgd', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])

  return model


with strategy.scope() :

  model = create_model()
  model.fit(x_train, y_train, epochs= 20)

 @mohammad008,
I was facing the indentation and other errors which executing the mentioned code on TPU environment. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0038f5f4e4d84e8a9fdbc5a5786fddda/untitled1912.ipynb) and provide the colab gist for quick debugging.
KerasTuner also supports data parallelism via [tf.distribute](https://www.tensorflow.org/tutorials/distribute/keras). Data parallelism and distributed tuning can be combined. For example, if you have 10 workers with 4 GPUs on each worker, you can run 10 parallel trials with each trial training on 4 GPUs by using [tf.distribute.MirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy). You can also run each trial on TPUs via [tf.distribute.TPUStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/TPUStrategy).

https://cloud.google.com/blog/products/ai-machine-learning/hyperparameter-tuning-using-tpus-cloud-ml-engine

https://keras.io/guides/keras_tuner/distributed_tuning/
 Thank you!  the issue is solved now, thanks ! 
the problem was that the part of code didnt contain the specified numbers of batch size and steps per epoch for TPU cluster training , by adding these hyperparameters for the fit() method , the issue will be solved : 

batch_size = 200
steps_per_epoch = 60000 // batch_size
validation_steps = 10000 // batch_size

it is recommended that the team explain these logics clearly on the document , thanks a lot  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/67301">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/67301">No</a>

### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.16.1

### Custom code

Yes

### OS platform and distribution

colab os platform

### 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

TPU

### Current behavior?

we expected that the training process , would be faster on multiple TPUs rather than single T4 GPU , but the numbers returned from fit() method would be the same . instead, we got faster calculations but whole different numbers returned , such as accuracy and loss significantly decreased after 5 epochs . in GPU , after 5 epochs we get 90+ percent of accuracy on test set , but on TPU mirrored strategy we get 24 % of accuracy on test set and it converges on this range

### Standalone code to reproduce the issue

```shell
# the whole project is private, we have a dataset on medical images and we split the dataset into into two # directories , then we create a keras datagenerator and create train_datagen and test_datagen . 
# then we load a model from keras.applications and add three Dense layers to it for classification task . # then we fine tune the model with adam optimizer with lr = 0.0001 . 
# the creation and compilation of the model are on the strategy.scope() , which strategy is created
# exactly from Tensorflow.org  "use TPU's" and mirrored strategy docs
```


### Relevant log output

_No response_