Inconsistency of `LeftShift` between eager mode and JIT=true
Hi **@zoux1a** ,

I was able to reproduce the issue on colab using TF v2.15, and TF-nightly. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/30f067f2c0c10cbc396fb8032b6e0b07/62869_2-15-nightly-v.ipynb) here for reference.

Thank you! Hi **@zoux1a** ,

- Ensure that the input tensors tensor_x and tensor_y have the correct shapes and data types. The LeftShift operation requires that the first tensor (x) be an integer tensor, and the second tensor (y) can be either an integer or a boolean tensor.
-  The raw_ops.LeftShift operation is used to perform bitwise left shift operations on tensors. However, this operation is only available for integer tensors. If you need to perform a bitwise left shift on a boolean tensor, you should use the bitwise.left_shift operation instead.
- Here i used bitwise.left_shift, it works fine. i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/e2e891a2ee396422b1da55014f63e20d/62869_2-16-1-v.ipynb) for your reference.

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. 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

2.14.1

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS (x86_64)

### 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?

Different outputs of `tf.raw_ops.LeftShift` between eagermode and op mode are observed.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

class Network(tf.Module):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, x, y):
      x = tf.raw_ops.LeftShift(y=x, x=y)        
      return x

m = Network()
tensor_x = tf.random.uniform([],minval=0,maxval=255,dtype=tf.int32)
tensor_y = tf.random.uniform([9],minval=0,maxval=255,dtype=tf.int32)
inp = {
    "x": tensor_x,
    "y": tensor_y
}

with tf.device('/CPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/CPU:0'):
        op_res = m(**inp)

    tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```


### Relevant log output

```shell
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(9,) dtype=float64) = '
-2147483648.0, 0.0, 0.0, ...
b'y (shape=(9,) dtype=float64) = '
0.0, 0.0, 0.0, ...
```
