NotFoundError: Graph execution error: TPU

While trying to run the following code on tpu-vm, it didn't work.

```python
tf: 2.15
keras: 3.0.5

tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu="local")
strategy = tf.distribute.TPUStrategy(tpu)

def get_compiled_model():
# Make a simple 2-layer densely-connected neural network.
inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(256, activation="relu")(inputs)
x = keras.layers.Dense(256, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
return model

def get_dataset():
batch_size = 32
num_val_samples = 10000

# Return the MNIST dataset in the form of a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(-1, 784).astype("float32") / 255
x_test = x_test.reshape(-1, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")

# Reserve num_val_samples samples for validation
x_val = x_train[-num_val_samples:]
y_val = y_train[-num_val_samples:]
x_train = x_train[:-num_val_samples]
y_train = y_train[:-num_val_samples]
return (
tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size),
)

with strategy.scope():
model_ = get_compiled_model()
train_dataset, val_dataset, test_dataset = get_dataset()

model_.fit(train_dataset, epochs=2, validation_data=val_dataset)
```
```
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
Cell In[5], line 1
----> 1 model_.fit(train_dataset, epochs=2, validation_data=val_dataset)

File /usr/local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:123, in filter_traceback..error_handler(*args, **kwargs)
120 filtered_tb = _process_traceback_frames(e.__traceback__)
121 # To get the full stack trace, call:
122 # `keras.config.disable_traceback_filtering()`
--> 123 raise e.with_traceback(filtered_tb) from None
124 finally:
125 del filtered_tb

File /usr/local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:

NotFoundError: Graph execution error:

Detected at node TPUReplicate/_compile/_9074053372847989778/_4 defined at (most recent call last):

```

首先，使用提供的 MNIST 範例，在 Python 3.10、TensorFlow 2.15、Keras 3.0.5 和本機 TPU VM 環境中重現回報的 model_.fit 失敗。檢查完整的 TPU 圖形執行錯誤，並判斷該失敗是否出在 TensorFlow/Keras/TPU 的互動上；確定可重現的原因和經驗證的解決方案即表示完成。

由索引模型根據 Issue 內容生成。