slient overflow occurs in tf.range leading to incorrect result, here is a possible fix
Your analysis and solution seem valid. Open a PR if you'd like, otherwise I can handle it I am afraid I don't have time in these few days, I would appreciate it if you can help me to open one. Hi @QuantumCoder4 , @Aloqeely 

I acknowledge the issue of overflow and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/7afd496e9453d4815948e8527be9d740/64081.ipynb) for reference.The proposed fix might work it seems. Please feel free to raise a PR. Hi @Aloqeely ,

I have a question. What happens if the value of delta =1 here keeping other two params same. It still might cause overflow ? Line 83 in `sequence_ops.cc`:
```python
if (delta > 0) {
      OP_REQUIRES(
          context, start <= limit,
          errors::InvalidArgument(
              "Requires start <= limit when delta > 0: ", start, "/", limit));
    }
```
In that case, this if statement would get triggered since delta = 1, and then `start <= limit` would get evaluated to `false` so the function will raise an error The proposed fix above won't work - it leads to different behavior.  See the discussion in the (now closed) PR #64144.

Someone can pick that up if they like.
### 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

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Following the documentation https://www.tensorflow.org/api_docs/python/tf/range, tf.range should have consistent result with np.arange. 
However, given the following inputs, tf.range outputs `tf.Tensor([1136033460], shape=(1,), dtype=int32)`, which is **incorrect**
The correct result is np.arange's output `[1136033460 -713794229]`.

I did some analysis and find the root cause lies in here:
https://github.com/tensorflow/tensorflow/blob/06e37b8d7f2ba0338efd0f588f068122414f0722/tensorflow/core/kernels/sequence_ops.cc#L100

When computing the `limit - start`, the actual value overflows.
To solve this problem, we can change the original code to a new one by applying the division first
origin:
```
Eigen::numext::ceil(Eigen::numext::abs((limit - start) / delta));
```

new:
```
Eigen::numext::ceil(Eigen::numext::abs(limit / delta - start / delta);
```

### Standalone code to reproduce the issue

```shell
import numpy as np
import tensorflow as tf
start = 1136033460
end = -2110457150
step = -1849827689
out = tf.range(start, end, step)
print(out)
out_np = np.arange(start, end, step)
print(out_np)
```
```


### Relevant log output

_No response_