XLA compiled `tf.reshape` throws ValueError: Shape must be rank 1 but is rank 0
@SuryanarayanaY  I was able to replicate the issue reported. Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/f0c6a28d05bd082dcb0f77e6ddb2f7af/xla-compiled-tf-reshape-throws-valueerror-shape-must-be-rank-1-but-is-rank-0.ipynb#scrollTo=7O8bcJB8WvA7). Thank you! Hi @dengyinlin ,

You need to pass shape as tuple to get it work as intended. With that modification the `tf.function` decorator works fine with both `jit_compile=True `or `jit_compile=False`. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/402d747df5cf8d09e2ed0903ebeb4caf/61856.ipynb). Hi @SuryanarayanaY ,
Thanks for the reply! Changing to `(4,)` can indeed solve the issue.

However, I also noticed that without compiling, `tf.reshape` can automatically convert the `(4)` to a tuple and the model can work fine. I think it would be good if such implicit conversion can be supported during compilation (`tf.function`), to ensure the model behavior is more consistent with or without compilation.  I agree to @dengyinlin comment.  It seems while tracing inside tf.function, its not able to convert (4) to (4,). I think the behaviour should be consistent with or without tf.function and I believe it can be fixed. Hi @dengyinlin ,

I agree to the fact that there should be consistency. We will deep dig into the issue and will update you. 

Thank you!
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf2.13, 2.15.0-dev20230913

### 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 behavior?

The following model calls `tf.reshape` on an input tensor, and throws ValueError when compiled with XLA. Without XLA compilation, it runs smoothly. 

This issue is observed on TF 2.13 and also nightly. See the [gist](https://colab.research.google.com/gist/dengyinlin/d9368054f16a011f9d1fed3cede96b95/xla-compiled-tf-reshape-throws-valueerror-shape-must-be-rank-1-but-is-rank-0.ipynb) for more detail.


### Standalone code to reproduce the issue

```python
class Model(tf.keras.Model):

  def __init__(self):
    super(Model, self).__init__()

  @tf.function(jit_compile=True)
  def call(self, x1):
    x2 = tf.reshape(x1, (2, 2))
    return tf.reshape(x2, (4))

m = Model()

input_shape = (4)
x1 = tf.constant([4.,5.,6.,7.], shape=input_shape)

y = m(x1)
print(y)
```


### Relevant log output

```shell
ValueError: Shape must be rank 1 but is rank 0 for '{{node Reshape_1}} = Reshape[T=DT_FLOAT, Tshape=DT_INT32](Reshape, Reshape_1/shape)' with input shapes: [2,2], [].
```
