We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Fix generating_index_arrays.test_numpy_diag_indices
|   |   |
|---|---|
|paddle|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
|tensorflow|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
|torch|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
|numpy|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
|jax|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py`
Content:
```
1 import ivy
2 from ivy.functional.frontends.numpy.func_wrapper import (
3     to_ivy_arrays_and_back,
4     outputs_to_numpy_arrays,
5 )
6 
7 
8 @to_ivy_arrays_and_back
9 def indices(dimensions, dtype=int, sparse=False):
10     dimensions = tuple(dimensions)
11     N = len(dimensions)
12     shape = (1,) * N
13     if sparse:
14         res = tuple()
15     else:
16         res = ivy.empty((N,) + dimensions, dtype=dtype)
17     for i, dim in enumerate(dimensions):
18         idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])
19         if sparse:
20             res = res + (idx,)
21         else:
22             res[i] = idx
23     return res
24 
25 
26 # unravel_index
27 @to_ivy_arrays_and_back
28 def unravel_index(indices, shape, order="C"):
29     ret = [x.astype("int64") for x in ivy.unravel_index(indices, shape)]
30     return tuple(ret)
31 
32 
33 @outputs_to_numpy_arrays
34 def diag_indices(n, ndim=2):
35     idx = ivy.arange(n, dtype=int)
36     return (idx,) * ndim
37 
38 
39 @to_ivy_arrays_and_back
40 def tril_indices(n, k=0, m=None):
41     return ivy.tril_indices(n, m, k)
42
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
--- a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
+++ b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
@@ -30,10 +30,12 @@
     return tuple(ret)
 
 
-@outputs_to_numpy_arrays
+@to_ivy_arrays_and_back
 def diag_indices(n, ndim=2):
-    idx = ivy.arange(n, dtype=int)
-    return (idx,) * ndim
+    idx = ivy.arange(n)
+    res = ivy.array((idx,) * ndim)
+    res = tuple(res.astype("int64"))
+    return res
 
 
 @to_ivy_arrays_and_back

{"golden_diff": "diff --git a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n--- a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n+++ b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n@@ -30,10 +30,12 @@\n     return tuple(ret)\n \n \n-@outputs_to_numpy_arrays\n+@to_ivy_arrays_and_back\n def diag_indices(n, ndim=2):\n-    idx = ivy.arange(n, dtype=int)\n-    return (idx,) * ndim\n+    idx = ivy.arange(n)\n+    res = ivy.array((idx,) * ndim)\n+    res = tuple(res.astype(\"int64\"))\n+    return res\n \n \n @to_ivy_arrays_and_back\n", "issue": "Fix generating_index_arrays.test_numpy_diag_indices\n|   |   |\r\n|---|---|\r\n|paddle|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|tensorflow|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|torch|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|numpy|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|jax|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n\n", "before_files": [{"content": "import ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n    to_ivy_arrays_and_back,\n    outputs_to_numpy_arrays,\n)\n\n\n@to_ivy_arrays_and_back\ndef indices(dimensions, dtype=int, sparse=False):\n    dimensions = tuple(dimensions)\n    N = len(dimensions)\n    shape = (1,) * N\n    if sparse:\n        res = tuple()\n    else:\n        res = ivy.empty((N,) + dimensions, dtype=dtype)\n    for i, dim in enumerate(dimensions):\n        idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])\n        if sparse:\n            res = res + (idx,)\n        else:\n            res[i] = idx\n    return res\n\n\n# unravel_index\n@to_ivy_arrays_and_back\ndef unravel_index(indices, shape, order=\"C\"):\n    ret = [x.astype(\"int64\") for x in ivy.unravel_index(indices, shape)]\n    return tuple(ret)\n\n\n@outputs_to_numpy_arrays\ndef diag_indices(n, ndim=2):\n    idx = ivy.arange(n, dtype=int)\n    return (idx,) * ndim\n\n\n@to_ivy_arrays_and_back\ndef tril_indices(n, k=0, m=None):\n    return ivy.tril_indices(n, m, k)\n", "path": "ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py"}], "after_files": [{"content": "import ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n    to_ivy_arrays_and_back,\n    outputs_to_numpy_arrays,\n)\n\n\n@to_ivy_arrays_and_back\ndef indices(dimensions, dtype=int, sparse=False):\n    dimensions = tuple(dimensions)\n    N = len(dimensions)\n    shape = (1,) * N\n    if sparse:\n        res = tuple()\n    else:\n        res = ivy.empty((N,) + dimensions, dtype=dtype)\n    for i, dim in enumerate(dimensions):\n        idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])\n        if sparse:\n            res = res + (idx,)\n        else:\n            res[i] = idx\n    return res\n\n\n# unravel_index\n@to_ivy_arrays_and_back\ndef unravel_index(indices, shape, order=\"C\"):\n    ret = [x.astype(\"int64\") for x in ivy.unravel_index(indices, shape)]\n    return tuple(ret)\n\n\n@to_ivy_arrays_and_back\ndef diag_indices(n, ndim=2):\n    idx = ivy.arange(n)\n    res = ivy.array((idx,) * ndim)\n    res = tuple(res.astype(\"int64\"))\n    return res\n\n\n@to_ivy_arrays_and_back\ndef tril_indices(n, k=0, m=None):\n    return ivy.tril_indices(n, m, k)\n", "path": "ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py"}]}