Inconsistency in XLA Compiled Models with `tf.matmul, tf.concat` with Extra Transpose Output
@sachinprasadhs I was able to replicate this error in [gpu](https://colab.research.google.com/gist/sushreebarsa/85c1c4c72b0b4a292476ba6ad3ad2783/62537-gpu.ipynb) sand [cpu](https://colab.research.google.com/gist/sushreebarsa/6c3a404872b5525c8a6a40c1f6dac369/62537.ipynb). Please find the attached gists.
Thank you! When the two outputs differ with the extra output, the mismatch is expected.
When you make the returns equal then there will not be any error. This is not a bug. 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/62537">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/62537">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS

### Mobile device

_No response_

### Python version

3.10.0

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I've encountered a bug exhibiting inconsistency under XLA compilation when using `tf.matmu` with extra transpose output.
In an effort to isolate the issue, we experimented with reducing the complexity of the models by altering the concatenation operation. 
We found that either removing one of the tensors involved in the `tf.concat` or `changing the shapes` of the tensors being concatenated prevents the error from occurring. 
This behavior suggests that the specific combination of tensor shapes and the concatenation operation could be the root cause for the inconsistency. 
I hope this information aids in pinpointing the root cause of the error.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2, inp3, inp4, inp5):
        concat = tf.concat([inp4, inp3, inp2, inp1], axis=0)
        matmul = tf.matmul(inp5, concat)
        out = tf.transpose(matmul, perm=[1, 0])
        extra_trans = tf.transpose(matmul, perm=[1, 0])
        return out, extra_trans,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2, inp3, inp4, inp5):
        concat = tf.concat([inp4, inp3, inp2, inp1], axis=0)
        matmul = tf.matmul(inp5, concat)
        out = tf.transpose(matmul, perm=[1, 0])
        return out, 

inputs = [
tf.random.uniform(shape=[1, 8], dtype=tf.float64),
tf.random.uniform(shape=[1, 8], dtype=tf.float64),
tf.random.uniform(shape=[1, 8], dtype=tf.float64),
tf.random.uniform(shape=[1, 8], dtype=tf.float64),
tf.random.uniform(shape=[61, 4], dtype=tf.float64),
]
model1 = Model1()
model2 = Model2()
device = "cpu"
with tf.device(device):
    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=0.001, atol=0.001, 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=0.001, atol=0.001, 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:2.15.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 485 / 488 (99.4%)
Max absolute difference: 1.85405643
Max relative difference: 14.07911993
 x: array([[0.760483, 1.281166, 1.877058, 1.311199, 2.140326, 1.761177,
        0.658837, 0.750503, 0.631657, 1.055799, 0.810465, 1.464139,
        0.645256, 1.319309, 1.004365, 0.408758, 0.189622, 0.616012,...
 y: array([[0.760483, 1.403855, 1.624952, 0.935791, 1.262206, 1.063867,
        0.501015, 0.444913, 1.281166, 1.38788 , 0.780569, 1.458368,
        1.646151, 1.374998, 1.941431, 1.119391, 1.877058, 0.899551,...
```
