Significant Output Discrepancies in `tf.math.reduce_prod + tf.tan` with Extra Transposed Tensor Output
@sachinprasadhs I was able to replicate this issue here. Please find the [gist](https://colab.research.google.com/gist/sushreebarsa/a53719eac0a0098382f7bdebc35e9c18/issue_61509.ipynb#scrollTo=pPWJfo6L7wrO). Thank you!
### 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

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

In TensorFlow 2.15.0 and 2.14.0, I've encountered a critical issue where adding an extra output of a transposed tensor in a model leads to **significant discrepancies** in the output under XLA compilation. This inconsistency is not observed in eager execution mode.
This error is reproducible using certain input tensors. , you can download the input tensor from [this link](https://github.com/GwiHwan-Go/repo/raw/main/issues/pickles/extra_transpose_output_err.pickle) 
Alternatively, you can replicate this issue using the [Colab notebook](https://colab.research.google.com/drive/1SZ-UNxfzreeK3llenVmDowSPEqHCJ2EY?usp=sharing) I have prepared.

### Standalone code to reproduce the issue

```python
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, inp1, inp2):
        conc = tf.concat([inp2, inp1], axis=4)
        reduced = tf.math.reduce_prod(conc, axis=4)
        taned = tf.tan(reduced)
        return taned,

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        transposed_inp1 = tf.transpose(inp1, perm=[4, 1, 2, 3, 0])
        transposed_inp2 = tf.transpose(inp2, perm=[4, 1, 2, 3, 0])
        transposed_conc = tf.concat([transposed_inp2, transposed_inp1], axis=0)
        conc = tf.transpose(transposed_conc, perm=[4, 1, 2, 3, 0])
        reduced = tf.math.reduce_prod(conc, axis=4)
        taned = tf.tan(reduced)
        return taned, conc,

model1 = Model1()
model2 = Model2()
device = tf.device(tf.config.list_logical_devices('CPU')[0].name)
pickle_file_path = 'extra_transpose_output_err.pickle' #YOUR_PICKLE_FILE_PATH
if not os.path.exists(pickle_file_path) :
    print(f'Pickle file not exist')
else :
    with open('extra_transpose_output_err.pickle', 'rb') as f :
        nparr = pickle.load(f)
    inputs = [tf.convert_to_tensor(arr) for arr in nparr]
    with 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)
        xla_out1 = model1(*inputs)
        xla_out2 = model2(*inputs)
        print(f'=========compiled_output(version:{tf.__version__})================')
        try :
            for i in range(min(len(xla_out1),len(xla_out2))):
                np.testing.assert_allclose(xla_out1[i].numpy(), xla_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)
```


### Relevant log output

```shell
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
2023-11-30 09:56:05.513431: I external/local_xla/xla/service/service.cc:168] XLA service 0x56088e88e500 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-11-30 09:56:05.513456: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701338165.528241 2713592 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
2023-11-30 09:56:05.528431: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.
=========compiled_output(version:2.15.0)================
XLA_eager triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 316 / 378 (83.6%)
Max absolute difference: 9560.12305077
Max relative difference: 52884.86608184
 x: array([[[[ 1.765686e-01]],

        [[ 1.316997e+00]],...
 y: array([[[[-2.580517e-01]],

        [[-5.574215e-01]],...


## COLAB OUTPUT ##
=========eager_output(version:2.14.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.14.0)================
XLA_eager triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 316 / 378 (83.6%)
Max absolute difference: 9560.12305077
Max relative difference: 52884.86608184
 x: array([[[[ 1.765686e-01]],

        [[ 1.316997e+00]],...
 y: array([[[[-2.580517e-01]],

        [[-5.574215e-01]],...
```
