We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
diag_indices_from

mask_indices


--- 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/jax/numpy/indexing.py`
Content:
```
1 # local
2 import ivy
3 from ivy.functional.frontends.jax.func_wrapper import (
4     to_ivy_arrays_and_back,
5 )
6 
7 
8 @to_ivy_arrays_and_back
9 def diagonal(a, offset=0, axis1=0, axis2=1):
10     return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)
11 
12 
13 @to_ivy_arrays_and_back
14 def diag(v, k=0):
15     return ivy.diag(v, k=k)
16 
17 
18 @to_ivy_arrays_and_back
19 def diag_indices(n, ndim=2):
20     idx = ivy.arange(n, dtype=int)
21     return (idx,) * ndim
22 
23 
24 # take_along_axis
25 @to_ivy_arrays_and_back
26 def take_along_axis(arr, indices, axis, mode="fill"):
27     return ivy.take_along_axis(arr, indices, axis, mode=mode)
28 
29 
30 @to_ivy_arrays_and_back
31 def tril_indices(n_rows, n_cols=None, k=0):
32     return ivy.tril_indices(n_rows, n_cols, k)
33 
34 
35 @to_ivy_arrays_and_back
36 def triu_indices(n, k=0, m=None):
37     return ivy.triu_indices(n, m, k)
38 
39 
40 @to_ivy_arrays_and_back
41 def triu_indices_from(arr, k=0):
42     return ivy.triu_indices(arr.shape[-2], arr.shape[-1], k)
43 
44 
45 def tril_indices_from(arr, k=0):
46     return ivy.tril_indices(arr.shape[-2], arr.shape[-1], k)
47 
48 
49 # unravel_index
50 @to_ivy_arrays_and_back
51 def unravel_index(indices, shape):
52     ret = [x.astype("int64") for x in ivy.unravel_index(indices, shape)]
53     return tuple(ret)
54
```
--- 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/jax/numpy/indexing.py b/ivy/functional/frontends/jax/numpy/indexing.py
--- a/ivy/functional/frontends/jax/numpy/indexing.py
+++ b/ivy/functional/frontends/jax/numpy/indexing.py
@@ -1,3 +1,6 @@
+# global
+import inspect
+
 # local
 import ivy
 from ivy.functional.frontends.jax.func_wrapper import (
@@ -42,6 +45,7 @@
     return ivy.triu_indices(arr.shape[-2], arr.shape[-1], k)
 
 
+@to_ivy_arrays_and_back
 def tril_indices_from(arr, k=0):
     return ivy.tril_indices(arr.shape[-2], arr.shape[-1], k)
 
@@ -51,3 +55,28 @@
 def unravel_index(indices, shape):
     ret = [x.astype("int64") for x in ivy.unravel_index(indices, shape)]
     return tuple(ret)
+
+
+@to_ivy_arrays_and_back
+def mask_indices(n, mask_func, k=0):
+    mask_func_obj = inspect.unwrap(mask_func)
+    mask_func_name = mask_func_obj.__name__
+    try:
+        ivy_mask_func_obj = getattr(ivy.functional.frontends.jax.numpy, mask_func_name)
+        a = ivy.ones((n, n))
+        mask = ivy_mask_func_obj(a, k=k)
+        indices = ivy.argwhere(mask.ivy_array)
+        return indices[:, 0], indices[:, 1]
+    except AttributeError as e:
+        print(f"Attribute error: {e}")
+
+
+@to_ivy_arrays_and_back
+def diag_indices_from(arr):
+    print(arr)
+    n = arr.shape[0]
+    ndim = ivy.get_num_dims(arr)
+    if not all(arr.shape[i] == n for i in range(ndim)):
+        raise ValueError("All dimensions of input must be of equal length")
+    idx = ivy.arange(n, dtype=int)
+    return (idx,) * ndim

{"golden_diff": "diff --git a/ivy/functional/frontends/jax/numpy/indexing.py b/ivy/functional/frontends/jax/numpy/indexing.py\n--- a/ivy/functional/frontends/jax/numpy/indexing.py\n+++ b/ivy/functional/frontends/jax/numpy/indexing.py\n@@ -1,3 +1,6 @@\n+# global\n+import inspect\n+\n # local\n import ivy\n from ivy.functional.frontends.jax.func_wrapper import (\n@@ -42,6 +45,7 @@\n     return ivy.triu_indices(arr.shape[-2], arr.shape[-1], k)\n \n \n+@to_ivy_arrays_and_back\n def tril_indices_from(arr, k=0):\n     return ivy.tril_indices(arr.shape[-2], arr.shape[-1], k)\n \n@@ -51,3 +55,28 @@\n def unravel_index(indices, shape):\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\n+def mask_indices(n, mask_func, k=0):\n+    mask_func_obj = inspect.unwrap(mask_func)\n+    mask_func_name = mask_func_obj.__name__\n+    try:\n+        ivy_mask_func_obj = getattr(ivy.functional.frontends.jax.numpy, mask_func_name)\n+        a = ivy.ones((n, n))\n+        mask = ivy_mask_func_obj(a, k=k)\n+        indices = ivy.argwhere(mask.ivy_array)\n+        return indices[:, 0], indices[:, 1]\n+    except AttributeError as e:\n+        print(f\"Attribute error: {e}\")\n+\n+\n+@to_ivy_arrays_and_back\n+def diag_indices_from(arr):\n+    print(arr)\n+    n = arr.shape[0]\n+    ndim = ivy.get_num_dims(arr)\n+    if not all(arr.shape[i] == n for i in range(ndim)):\n+        raise ValueError(\"All dimensions of input must be of equal length\")\n+    idx = ivy.arange(n, dtype=int)\n+    return (idx,) * ndim\n", "issue": "diag_indices_from\n\nmask_indices\n\n", "before_files": [{"content": "# local\nimport ivy\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@to_ivy_arrays_and_back\ndef diagonal(a, offset=0, axis1=0, axis2=1):\n    return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)\n\n\n@to_ivy_arrays_and_back\ndef diag(v, k=0):\n    return ivy.diag(v, k=k)\n\n\n@to_ivy_arrays_and_back\ndef diag_indices(n, ndim=2):\n    idx = ivy.arange(n, dtype=int)\n    return (idx,) * ndim\n\n\n# take_along_axis\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis, mode=\"fill\"):\n    return ivy.take_along_axis(arr, indices, axis, mode=mode)\n\n\n@to_ivy_arrays_and_back\ndef tril_indices(n_rows, n_cols=None, k=0):\n    return ivy.tril_indices(n_rows, n_cols, k)\n\n\n@to_ivy_arrays_and_back\ndef triu_indices(n, k=0, m=None):\n    return ivy.triu_indices(n, m, k)\n\n\n@to_ivy_arrays_and_back\ndef triu_indices_from(arr, k=0):\n    return ivy.triu_indices(arr.shape[-2], arr.shape[-1], k)\n\n\ndef tril_indices_from(arr, k=0):\n    return ivy.tril_indices(arr.shape[-2], arr.shape[-1], k)\n\n\n# unravel_index\n@to_ivy_arrays_and_back\ndef unravel_index(indices, shape):\n    ret = [x.astype(\"int64\") for x in ivy.unravel_index(indices, shape)]\n    return tuple(ret)\n", "path": "ivy/functional/frontends/jax/numpy/indexing.py"}], "after_files": [{"content": "# global\nimport inspect\n\n# local\nimport ivy\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@to_ivy_arrays_and_back\ndef diagonal(a, offset=0, axis1=0, axis2=1):\n    return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)\n\n\n@to_ivy_arrays_and_back\ndef diag(v, k=0):\n    return ivy.diag(v, k=k)\n\n\n@to_ivy_arrays_and_back\ndef diag_indices(n, ndim=2):\n    idx = ivy.arange(n, dtype=int)\n    return (idx,) * ndim\n\n\n# take_along_axis\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis, mode=\"fill\"):\n    return ivy.take_along_axis(arr, indices, axis, mode=mode)\n\n\n@to_ivy_arrays_and_back\ndef tril_indices(n_rows, n_cols=None, k=0):\n    return ivy.tril_indices(n_rows, n_cols, k)\n\n\n@to_ivy_arrays_and_back\ndef triu_indices(n, k=0, m=None):\n    return ivy.triu_indices(n, m, k)\n\n\n@to_ivy_arrays_and_back\ndef triu_indices_from(arr, k=0):\n    return ivy.triu_indices(arr.shape[-2], arr.shape[-1], k)\n\n\n@to_ivy_arrays_and_back\ndef tril_indices_from(arr, k=0):\n    return ivy.tril_indices(arr.shape[-2], arr.shape[-1], k)\n\n\n# unravel_index\n@to_ivy_arrays_and_back\ndef unravel_index(indices, shape):\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 mask_indices(n, mask_func, k=0):\n    mask_func_obj = inspect.unwrap(mask_func)\n    mask_func_name = mask_func_obj.__name__\n    try:\n        ivy_mask_func_obj = getattr(ivy.functional.frontends.jax.numpy, mask_func_name)\n        a = ivy.ones((n, n))\n        mask = ivy_mask_func_obj(a, k=k)\n        indices = ivy.argwhere(mask.ivy_array)\n        return indices[:, 0], indices[:, 1]\n    except AttributeError as e:\n        print(f\"Attribute error: {e}\")\n\n\n@to_ivy_arrays_and_back\ndef diag_indices_from(arr):\n    print(arr)\n    n = arr.shape[0]\n    ndim = ivy.get_num_dims(arr)\n    if not all(arr.shape[i] == n for i in range(ndim)):\n        raise ValueError(\"All dimensions of input must be of equal length\")\n    idx = ivy.arange(n, dtype=int)\n    return (idx,) * ndim\n", "path": "ivy/functional/frontends/jax/numpy/indexing.py"}]}