XLA doesn't do the DCE as autocluster
@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/bc8d4335093bf160f96c76455d156322/61884.ipynb). Thank you! @YangChenyuan ,

I can able to replicate the behaviour with `jit_compile=True` passed to tf.function. With `jit_compile=False` its working fine. It seems issue with XLA and will triage accordingly.

Attaching [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fdda23e3e415b84b0057ae167f40be91/61884_r1.ipynb) for same. 

Thank you!

### 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?

XLA doesn't do the DCE as autocluster. In the example below, the second line `sliced2 = tf.slice(x1, [0, 1, 0, 0], [-1, -1, 4, -1])` is dead code, which is deleted when enabling `autocluster`. However, the XLA compiled model will still execute this line.

It is expected to delete this line since it is dead code.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import os

"""
Autocluster
"""

os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit'

class Model(tf.keras.Model):

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

  @tf.function
  def call(self, x1):
    sliced1 = tf.slice(x1, [0, 0, 1, 0], [-1, -1, 1, -1])
    sliced2 = tf.slice(x1, [0, 1, 0, 0], [-1, -1, 4, -1])
    return sliced1

# Initializing the model
x1_shape = (1, 3, 3, 2)
m = Model()

# Inputs to the model
x1 = tf.range(18)
x1 = tf.reshape(x1, x1_shape)

# Call model
output_tensor = m(x1)

"""
XLA
"""
os.environ['TF_XLA_FLAGS'] = ''

class Model(tf.keras.Model):

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

  @tf.function(jit_compile=True)
  def call(self, x1):
    sliced1 = tf.slice(x1, [0, 0, 1, 0], [-1, -1, 1, -1])
    sliced2 = tf.slice(x1, [0, 1, 0, 0], [-1, -1, 4, -1])
    return sliced1

# Initializing the model
x1_shape = (1, 3, 3, 2)
m = Model()

# Inputs to the model
x1 = tf.range(18)
x1 = tf.reshape(x1, x1_shape)

# Call model
output_tensor = m(x1)

"""
InvalidArgumentError: Exception encountered when calling layer 'model_5' (type Model).

Expected size[2] in [0, 3], but got 4
"""
```


### Relevant log output

_No response_