Inconsistency in TensorFlow's Handling of Distributive Properties(`a(b+c) vs ab + ac`) in XLA compiled model
@tilakrayal Hey, I was wondering, have you reproduced this issue? @Gwihwan-Go,
Hi,

Providing additional outputs does not result in matching outputs. This behavior is expected. Inside the call function you are generating the value for tensor, which in each calls generates different random values because XLA currently ignores TF seeds to random operations which makes the output different for obvious reason. Please refer [known](https://www.tensorflow.org/xla/known_issues#random_number_generation_ignores_tf_seed) issues from XLA section. Also Providing additional outputs does not result in matching outputs. This behavior is expected. 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?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62467">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62467">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.14

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS (x86_64)

### Mobile device

_No response_

### Python version

 3.10.12 

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I've discovered an inconsistency in TensorFlow's computation, specifically in how distributive properties are handled in tensor operations. Two models, `Model1` and `Model2`, which are mathematically equivalent under the distributive law, are producing different results when compiled with XLA on certain input set(please donwload this [pickle file](https://github.com/GwiHwan-Go/repo/raw/main/0.pickle)).

This behavior is also seen on colab with TF 2.14.0
```python
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        v0_0 = tf.abs(inp)
        v2_0 = tf.negative(inp)
        v4_0 = tf.add(v0_0, inp)
        v5_0 = tf.multiply(v2_0, v4_0)
        return v5_0
# Represents: (abs(inp) + inp) * (-inp)
```

```python 
class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        v2_0 = tf.negative(inp)
        v3_0 = tf.abs(inp)
        v4_0 = tf.multiply(v3_0, v2_0)
        v5_0 = tf.multiply(inp, v2_0)
        v6_0 = tf.add(v4_0, v5_0)
        return v6_0
# Represents: -inp * abs(inp) + inp * -inp
```

### Standalone code to reproduce the issue

```python
## After download the pickle file 

from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [17, 64, 59, 1, 1] : float32
        v0_0 = tf.abs(inp)
        v2_0 = tf.negative(inp)
        v4_0 = tf.add(v0_0, inp)
        v5_0 = tf.multiply(v2_0, v4_0)
        return v5_0

# (abs(inp) + inp) * (-inp )

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [17, 64, 59, 1, 1] : float32
        v2_0 = tf.negative(inp)
        v3_0 = tf.abs(inp)
        v4_0 = tf.multiply(v3_0, v2_0)
        v5_0 = tf.multiply(inp, v2_0)
        v6_0 = tf.add(v4_0, v5_0)
        return v6_0
# -inp * abs(inp) + inp * -inp 

model1 = Model1()
model2 = Model2()

pickle_file_path = YOUR_PICKLE_FILE_PATH
if not os.path.exists(pickle_file_path) :
    print(f'Pickle file not exist')
else :
    with open(pickle_file_path, 'rb') as f :
        np_arrs1 = pickle.load(f)
    inputs = [tf.convert_to_tensor(arr) for arr in np_arrs1.values()]
    with tf.device('cpu'):
        tf.config.run_functions_eagerly(True)
        out1 = model1(*inputs)
        out2 = model2(*inputs)
        print(f'=========eager_output(version:{tf.__version__})================')
        try :
            for i in range(min(len(out1),len(out2))):
                np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.01, err_msg=f'at checking {i}th')
            print("XLA_eager does not trigger assertion")
        except AssertionError as e:
            print("XLA_eager triggers assertion")
            print(e)
        tf.config.run_functions_eagerly(False)
        out1 = model1(*inputs)
        out2 = model2(*inputs)
        print(f'=========compiled_output(version:{tf.__version__})================')
        try :
            for i in range(min(len(out1),len(out2))):
                np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.01, err_msg=f'at checking {i}th')
            print("XLA_complie does not trigger assertion")
        except AssertionError as e:
            print("XLA_complie triggers assertion")
            print(e)
```


### Relevant log output

```shell
=========eager_output(version:2.14.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.14.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.01, atol=0
at checking 0th
Mismatched elements: 1891 / 3776 (50.1%)
Max absolute difference: 4.5960763e-07
Max relative difference: 1.
 x: array([[[[ 0.000000e+00]],

        [[ 0.000000e+00]],...
 y: array([[[[-2.805131e-09]],

        [[-3.204883e-08]],...
```
