Compiled Model throws `pred must not be a Python bool`
@YangChenyuan ,

Inside the `tf.function` python statements will be executed only in first stage i.e retracing stage while capturing tensorflow operations in graph and in second stage of execution only tensorflow operation will executed. This might be the reason for the exception raised. If you pass boolean tensor instead of Python bool and it works fine as expected.

Please refer to the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fa460fdb1a12cf2a630119223ffa8d2b/61863.ipynb#scrollTo=7trxEkCL7Hzl). Please let us know if you still got any queries.

Thank you!
 Hi @SuryanarayanaY, thanks for your further explanation! I wonder if the autograph **should** trace these primitive types in the model since such primitive types, like `bool`, `int`, are very basic and used everywhere. I expect that they will be used without any error when using XLA compilation. Hi @YangChenyuan ,

`tf.cond(pred)` raises `TypeError` if `pred` is not a callable. Here `pred` is python bool and hence the error. 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. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0-dev20230914

### 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?

I have a `tf.cond` in the model with a constant `pred = True`. After `tf.function(jit_compile=True)`, it raises the error `TypeError: ('pred must not be a Python bool', True)`.

By contrast, there isn't any error if the model is executed without the optimization.

### Standalone code to reproduce the issue

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

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

  @tf.function(jit_compile=True) # Comment this line, it will succeed
  def call(self, x):
    pred = True
    x1 = x
    y = tf.cond(pred, lambda: x1, lambda: x1)
    return y

m = Model()
input_shape = [4]
x = tf.constant([4.,5.,6.,7.], shape=input_shape)
y = m(x)
```


### Relevant log output

```shell
TypeError: Exception encountered when calling layer 'model_11' (type Model).

in user code:

    File "<ipython-input-14-5a3ae524deb3>", line 10, in call  *
        y = tf.cond(pred, lambda: x1, lambda: x1)

    TypeError: ('pred must not be a Python bool', True)


Call arguments received by layer 'model_11' (type Model):
  • x=tf.Tensor(shape=(4,), dtype=float32)
```
