Issue with Reproducible Results: Inconsistent Behavior of Random Seeds in TensorFlow
@soran-ghaderi,
**tf.random.set_seed** sets the global random seed. Calling [tf.keras.utils.set_random_seed](https://www.tensorflow.org/api_docs/python/tf/keras/utils/set_random_seed) sets the Python seed, the NumPy seed, and the TensorFlow seed. Setting these seeds is necessary to ensure any random numbers your program generates are also deterministic.

Could you please have a look at this official document for reference.
https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism

Thank you! Thank you @tilakrayal, I labeled this issue a bug as deterministic behavior is expected when calling tf.random.set_seed and is more logical to be the default. I was able to get the consistent output with only `tf.random.set_seed(1)` using `tf-nightly`, tested with multiple runs.

```
import tensorflow as tf
print(tf.__version__)
import random

tf.random.set_seed(1)
# random.seed(42) # This line seems to be redundant

x = tf.constant(tf.random.uniform([2, 3, 2]), dtype=tf.float32)
print(x)
```

Could you please test with the nightly version and let us know if you still observe the same behavior. 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?
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### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.12

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.8-3.9-3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I would like to report an issue regarding the reproducibility of results in TensorFlow. Currently, in order to achieve consistent and deterministic results, it seems necessary to set both random.seed(42) and tf.random.set_seed(1) together.

Expected Behavior:
Setting tf.random.set_seed(1) alone should be sufficient to ensure reproducible results across different runs.

Observed Behavior:
Without setting random.seed(42) alongside tf.random.set_seed(1), the results obtained from TensorFlow exhibit inconsistency and do not remain fixed between runs.



### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import random

tf.random.set_seed(1)
random.seed(42) # This line seems to be redundant

x = tf.constant(tf.random.uniform([2, 3, 2]), dtype=tf.float32)
```


### Relevant log output

_No response_