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


--- 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/indexing_like_operations.py`
Content:
```
1 import ivy
2 from ivy.functional.frontends.numpy.func_wrapper import (
3     to_ivy_arrays_and_back,
4     inputs_to_ivy_arrays,
5 )
6 
7 
8 @to_ivy_arrays_and_back
9 def take_along_axis(arr, indices, axis):
10     return ivy.take_along_axis(arr, indices, axis)
11 
12 
13 @to_ivy_arrays_and_back
14 def tril_indices(n, k=0, m=None):
15     return ivy.tril_indices(n, m, k)
16 
17 
18 @to_ivy_arrays_and_back
19 def indices(dimensions, dtype=int, sparse=False):
20     dimensions = tuple(dimensions)
21     N = len(dimensions)
22     shape = (1,) * N
23     if sparse:
24         res = tuple()
25     else:
26         res = ivy.empty((N,) + dimensions, dtype=dtype)
27     for i, dim in enumerate(dimensions):
28         idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])
29         if sparse:
30             res = res + (idx,)
31         else:
32             res[i] = idx
33     return res
34 
35 
36 # unravel_index
37 @to_ivy_arrays_and_back
38 def unravel_index(indices, shape, order="C"):
39     ret = [x.astype("int64") for x in ivy.unravel_index(indices, shape)]
40     return tuple(ret)
41 
42 
43 @to_ivy_arrays_and_back
44 def fill_diagonal(a, val, wrap=False):
45     if a.ndim < 2:
46         raise ValueError("array must be at least 2-d")
47     end = None
48     if a.ndim == 2:
49         # Explicit, fast formula for the common case.  For 2-d arrays, we
50         # accept rectangular ones.
51         step = a.shape[1] + 1
52         # This is needed to don't have tall matrix have the diagonal wrap.
53         if not wrap:
54             end = a.shape[1] * a.shape[1]
55     else:
56         # For more than d=2, the strided formula is only valid for arrays with
57         # all dimensions equal, so we check first.
58         if not ivy.all(ivy.diff(a.shape) == 0):
59             raise ValueError("All dimensions of input must be of equal length")
60         step = 1 + ivy.sum(ivy.cumprod(a.shape[:-1]))
61 
62     # Write the value out into the diagonal.
63     shape = a.shape
64     a = ivy.reshape(a, a.size)
65     a[:end:step] = val
66     a = ivy.reshape(a, shape)
67 
68 
69 @inputs_to_ivy_arrays
70 def put_along_axis(arr, indices, values, axis):
71     ivy.put_along_axis(arr, indices, values, axis)
72 
73 
74 def diag(v, k=0):
75     return ivy.diag(v, k=k)
76 
77 
78 @to_ivy_arrays_and_back
79 def diagonal(a, offset, axis1, axis2):
80     return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)
81
```
--- 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/indexing_like_operations.py b/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py
--- a/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py
+++ b/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py
@@ -2,6 +2,7 @@
 from ivy.functional.frontends.numpy.func_wrapper import (
     to_ivy_arrays_and_back,
     inputs_to_ivy_arrays,
+    handle_numpy_out,
 )
 
 
@@ -78,3 +79,22 @@
 @to_ivy_arrays_and_back
 def diagonal(a, offset, axis1, axis2):
     return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)
+
+
+@to_ivy_arrays_and_back
+@handle_numpy_out
+def compress(condition, a, axis=None, out=None):
+    condition_arr = ivy.asarray(condition).astype(bool)
+    if condition_arr.ndim != 1:
+        raise ivy.utils.exceptions.IvyException("Condition must be a 1D array")
+    if axis is None:
+        arr = ivy.asarray(a).flatten()
+        axis = 0
+    else:
+        arr = ivy.moveaxis(a, axis, 0)
+    if condition_arr.shape[0] > arr.shape[0]:
+        raise ivy.utils.exceptions.IvyException(
+            "Condition contains entries that are out of bounds"
+        )
+    arr = arr[: condition_arr.shape[0]]
+    return ivy.moveaxis(arr[condition_arr], 0, axis)

{"golden_diff": "diff --git a/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py b/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py\n--- a/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py\n+++ b/ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py\n@@ -2,6 +2,7 @@\n from ivy.functional.frontends.numpy.func_wrapper import (\n     to_ivy_arrays_and_back,\n     inputs_to_ivy_arrays,\n+    handle_numpy_out,\n )\n \n \n@@ -78,3 +79,22 @@\n @to_ivy_arrays_and_back\n def diagonal(a, offset, axis1, axis2):\n     return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)\n+\n+\n+@to_ivy_arrays_and_back\n+@handle_numpy_out\n+def compress(condition, a, axis=None, out=None):\n+    condition_arr = ivy.asarray(condition).astype(bool)\n+    if condition_arr.ndim != 1:\n+        raise ivy.utils.exceptions.IvyException(\"Condition must be a 1D array\")\n+    if axis is None:\n+        arr = ivy.asarray(a).flatten()\n+        axis = 0\n+    else:\n+        arr = ivy.moveaxis(a, axis, 0)\n+    if condition_arr.shape[0] > arr.shape[0]:\n+        raise ivy.utils.exceptions.IvyException(\n+            \"Condition contains entries that are out of bounds\"\n+        )\n+    arr = arr[: condition_arr.shape[0]]\n+    return ivy.moveaxis(arr[condition_arr], 0, axis)\n", "issue": "compress\n\n", "before_files": [{"content": "import ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n    to_ivy_arrays_and_back,\n    inputs_to_ivy_arrays,\n)\n\n\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis):\n    return ivy.take_along_axis(arr, indices, axis)\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\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 fill_diagonal(a, val, wrap=False):\n    if a.ndim < 2:\n        raise ValueError(\"array must be at least 2-d\")\n    end = None\n    if a.ndim == 2:\n        # Explicit, fast formula for the common case.  For 2-d arrays, we\n        # accept rectangular ones.\n        step = a.shape[1] + 1\n        # This is needed to don't have tall matrix have the diagonal wrap.\n        if not wrap:\n            end = a.shape[1] * a.shape[1]\n    else:\n        # For more than d=2, the strided formula is only valid for arrays with\n        # all dimensions equal, so we check first.\n        if not ivy.all(ivy.diff(a.shape) == 0):\n            raise ValueError(\"All dimensions of input must be of equal length\")\n        step = 1 + ivy.sum(ivy.cumprod(a.shape[:-1]))\n\n    # Write the value out into the diagonal.\n    shape = a.shape\n    a = ivy.reshape(a, a.size)\n    a[:end:step] = val\n    a = ivy.reshape(a, shape)\n\n\n@inputs_to_ivy_arrays\ndef put_along_axis(arr, indices, values, axis):\n    ivy.put_along_axis(arr, indices, values, axis)\n\n\ndef diag(v, k=0):\n    return ivy.diag(v, k=k)\n\n\n@to_ivy_arrays_and_back\ndef diagonal(a, offset, axis1, axis2):\n    return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)\n", "path": "ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py"}], "after_files": [{"content": "import ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n    to_ivy_arrays_and_back,\n    inputs_to_ivy_arrays,\n    handle_numpy_out,\n)\n\n\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis):\n    return ivy.take_along_axis(arr, indices, axis)\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\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 fill_diagonal(a, val, wrap=False):\n    if a.ndim < 2:\n        raise ValueError(\"array must be at least 2-d\")\n    end = None\n    if a.ndim == 2:\n        # Explicit, fast formula for the common case.  For 2-d arrays, we\n        # accept rectangular ones.\n        step = a.shape[1] + 1\n        # This is needed to don't have tall matrix have the diagonal wrap.\n        if not wrap:\n            end = a.shape[1] * a.shape[1]\n    else:\n        # For more than d=2, the strided formula is only valid for arrays with\n        # all dimensions equal, so we check first.\n        if not ivy.all(ivy.diff(a.shape) == 0):\n            raise ValueError(\"All dimensions of input must be of equal length\")\n        step = 1 + ivy.sum(ivy.cumprod(a.shape[:-1]))\n\n    # Write the value out into the diagonal.\n    shape = a.shape\n    a = ivy.reshape(a, a.size)\n    a[:end:step] = val\n    a = ivy.reshape(a, shape)\n\n\n@inputs_to_ivy_arrays\ndef put_along_axis(arr, indices, values, axis):\n    ivy.put_along_axis(arr, indices, values, axis)\n\n\ndef diag(v, k=0):\n    return ivy.diag(v, k=k)\n\n\n@to_ivy_arrays_and_back\ndef diagonal(a, offset, axis1, axis2):\n    return ivy.diagonal(a, offset=offset, axis1=axis1, axis2=axis2)\n\n\n@to_ivy_arrays_and_back\n@handle_numpy_out\ndef compress(condition, a, axis=None, out=None):\n    condition_arr = ivy.asarray(condition).astype(bool)\n    if condition_arr.ndim != 1:\n        raise ivy.utils.exceptions.IvyException(\"Condition must be a 1D array\")\n    if axis is None:\n        arr = ivy.asarray(a).flatten()\n        axis = 0\n    else:\n        arr = ivy.moveaxis(a, axis, 0)\n    if condition_arr.shape[0] > arr.shape[0]:\n        raise ivy.utils.exceptions.IvyException(\n            \"Condition contains entries that are out of bounds\"\n        )\n    arr = arr[: condition_arr.shape[0]]\n    return ivy.moveaxis(arr[condition_arr], 0, axis)\n", "path": "ivy/functional/frontends/numpy/indexing_routines/indexing_like_operations.py"}]}