Different Behavior of tf.raw_ops.IgammaGradA
I observe similar result in both `jit_compile = True` and `jit_compile = False`, since when compiling with `JIT` takes different code path and involves `fusion` operation and `casting` `dtype` to specific `dtype`, based on all these conditions, you can't expect the same result with `JIT` and without `JIT`.
Attaching the Gist [here](https://colab.sandbox.google.com/gist/sachinprasadhs/1f64cb8c1d286ba5de2a240ff915f5d9/-tf-raw_ops-igammagrada.ipynb) for comparison. Thanks! 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/62278">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/62278">No</a>

### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.14.0

### 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

11.8

### GPU model and memory

GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070

### Current behavior?

When the **tf.raw_ops.IgammaGradA** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.

### Standalone code to reproduce the issue

```shell
#this issue is about tf.raw_ops.IgammaGradA
import tensorflow as tf
import traceback

def replace_special_values(tensor):
    # Convert tensor to tf.float32 if it's not a supported dtype
    supported_dtypes = [tf.float16, tf.float32, tf.float64, tf.bfloat16]
    if tensor.dtype not in supported_dtypes:
        original_dtype = tensor.dtype
        tensor = tf.cast(tensor, tf.float32)
    else :
        original_dtype = None
    
    # Replace NaNs with zeros
    tensor = tf.where(tf.math.is_nan(tensor), tf.zeros_like(tensor), tensor)
    
    # Replace positive infinities with a large number (e.g., 1e30)
    tensor = tf.where(tf.math.is_inf(tensor), 100, tensor)
    
    # Replace negative infinities with a small number (e.g., -1e30)
    tensor = tf.where(tf.math.is_inf(tensor) & tf.math.less(tensor, 0), -100, tensor)
    
    # Convert tensor back to its original dtype
    if original_dtype is not None :
        tensor = tf.cast(tensor, original_dtype)
    return tensor

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

    @tf.function(jit_compile=True)
    def __call__(self, x):
      
      x = tf.raw_ops.IgammaGradA(x=x, a=tf.random.normal([1, 3, 6, 6, 1, 4, 6, 2, 1, 1], dtype=tf.float32))        
      return x

is_valid = True
inf = float('inf')
m = Network()
inp = {
    "x": tf.random.normal([10, 9], dtype=tf.float32),
    }

with tf.device('/GPU:0'):
    tf.config.run_functions_eagerly(True)
    try :
        no_op_res = m(**inp)
    except :
        print(traceback.format_exc())
        # Generated ops in forward is invalid, generated code is invalid. 
        is_valid=False
if is_valid : 
    tf.config.run_functions_eagerly(False)
    with tf.device('/GPU:0'):
        op_res = m(**inp)

    if isinstance(no_op_res, tf.Tensor) and isinstance(op_res, tf.Tensor) :
        no_op_res = replace_special_values(no_op_res)
        op_res = replace_special_values(op_res)
        tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
    print("no_error")
```


### Relevant log output

```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 60, in <module>
    tf.debugging.assert_near(no_op_res, op_res, atol=0.001, rtol=0.001)
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
    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=float32), atol = tf.Tensor(0.001, shape=(), dtype=float32)'
b'x (shape=(1, 3, 6, 6, 1, 4, 6, 2, 10, 9) dtype=float32) = '
0.0, 0.0, 0.0, ...
b'y (shape=(1, 3, 6, 6, 1, 4, 6, 2, 10, 9) dtype=float32) = '
0.0, 0.0, 0.0, ...
```
