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


--- 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/devicearray.py`
Content:
```
1 # global
2 
3 # local
4 import ivy
5 import ivy.functional.frontends.jax as jax_frontend
6 
7 
8 class DeviceArray:
9     def __init__(self, array, weak_type=False):
10         self._ivy_array = array if isinstance(array, ivy.Array) else ivy.array(array)
11         self.weak_type = weak_type
12 
13     def __repr__(self):
14         main = (
15             str(self.ivy_array.__repr__())
16             .replace("ivy.array", "ivy.frontends.jax.DeviceArray")
17             .replace(")", "")
18             + ", dtype="
19             + str(self.ivy_array.dtype)
20         )
21         if self.weak_type:
22             return main + ", weak_type=True)"
23         return main + ")"
24 
25     # Properties #
26     # ---------- #
27 
28     @property
29     def ivy_array(self):
30         return self._ivy_array
31 
32     @property
33     def dtype(self):
34         return self.ivy_array.dtype
35 
36     @property
37     def shape(self):
38         return self.ivy_array.shape
39 
40     @property
41     def at(self):
42         return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)
43 
44     # Instance Methods #
45     # ---------------- #
46 
47     def all(self, *, axis=None, out=None, keepdims=False):
48         return jax_frontend.numpy.all(
49             self._ivy_array, axis=axis, keepdims=keepdims, out=out
50         )
51 
52     def argmax(
53         self,
54         /,
55         *,
56         axis=None,
57         out=None,
58         keepdims=False,
59     ):
60         return jax_frontend.numpy.argmax(
61             self,
62             axis=axis,
63             out=out,
64             keepdims=keepdims,
65         )
66 
67     def conj(self, /):
68         return jax_frontend.numpy.conj(self._ivy_array)
69 
70     def conjugate(self, /):
71         return jax_frontend.numpy.conjugate(self._ivy_array)
72 
73     def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, where=None):
74         return jax_frontend.numpy.mean(
75             self._ivy_array,
76             axis=axis,
77             dtype=dtype,
78             out=out,
79             keepdims=keepdims,
80             where=where,
81         )
82 
83     def cumprod(self, axis=None, dtype=None, out=None):
84         return jax_frontend.numpy.cumprod(
85             self,
86             axis=axis,
87             dtype=dtype,
88             out=out,
89         )
90 
91     def cumsum(self, axis=None, dtype=None, out=None):
92         return jax_frontend.numpy.cumsum(
93             self,
94             axis=axis,
95             dtype=dtype,
96             out=out,
97         )
98 
99     def nonzero(self, *, size=None, fill_value=None):
100         return jax_frontend.numpy.nonzero(
101             self,
102             size=size,
103             fill_value=fill_value,
104         )
105 
106     def ravel(self, order="C"):
107         return jax_frontend.numpy.ravel(
108             self,
109             order=order,
110         )
111 
112     def sort(self, axis=-1, order=None):
113         return jax_frontend.numpy.sort(
114             self,
115             axis=axis,
116             order=order,
117         )
118 
119     def __add__(self, other):
120         return jax_frontend.numpy.add(self, other)
121 
122     def __radd__(self, other):
123         return jax_frontend.numpy.add(other, self)
124 
125     def __sub__(self, other):
126         return jax_frontend.lax.sub(self, other)
127 
128     def __rsub__(self, other):
129         return jax_frontend.lax.sub(other, self)
130 
131     def __mul__(self, other):
132         return jax_frontend.lax.mul(self, other)
133 
134     def __rmul__(self, other):
135         return jax_frontend.lax.mul(other, self)
136 
137     def __div__(self, other):
138         return jax_frontend.numpy.divide(self, other)
139 
140     def __rdiv__(self, other):
141         return jax_frontend.numpy.divide(other, self)
142 
143     def __mod__(self, other):
144         return jax_frontend.numpy.mod(self, other)
145 
146     def __rmod__(self, other):
147         return jax_frontend.numpy.mod(other, self)
148 
149     def __truediv__(self, other):
150         return jax_frontend.numpy.divide(self, other)
151 
152     def __rtruediv__(self, other):
153         return jax_frontend.numpy.divide(other, self)
154 
155     def __matmul__(self, other):
156         return jax_frontend.numpy.dot(self, other)
157 
158     def __rmatmul__(self, other):
159         return jax_frontend.numpy.dot(other, self)
160 
161     def __pos__(self):
162         return self
163 
164     def __neg__(self):
165         return jax_frontend.lax.neg(self)
166 
167     def __eq__(self, other):
168         return jax_frontend.lax.eq(self, other)
169 
170     def __ne__(self, other):
171         return jax_frontend.lax.ne(self, other)
172 
173     def __lt__(self, other):
174         return jax_frontend.lax.lt(self, other)
175 
176     def __le__(self, other):
177         return jax_frontend.lax.le(self, other)
178 
179     def __gt__(self, other):
180         return jax_frontend.lax.gt(self, other)
181 
182     def __ge__(self, other):
183         return jax_frontend.lax.ge(self, other)
184 
185     def __abs__(self):
186         return jax_frontend.numpy.abs(self)
187 
188     def __pow__(self, other):
189         return jax_frontend.lax.pow(self, other)
190 
191     def __rpow__(self, other):
192         other = ivy.asarray(other)
193         return jax_frontend.lax.pow(other, self)
194 
195     def __and__(self, other):
196         return jax_frontend.numpy.bitwise_and(self, other)
197 
198     def __rand__(self, other):
199         return jax_frontend.numpy.bitwise_and(other, self)
200 
201     def __or__(self, other):
202         return jax_frontend.numpy.bitwise_or(self, other)
203 
204     def __ror__(self, other):
205         return jax_frontend.numpy.bitwise_or(other, self)
206 
207     def __xor__(self, other):
208         return jax_frontend.lax.bitwise_xor(self, other)
209 
210     def __rxor__(self, other):
211         return jax_frontend.lax.bitwise_xor(other, self)
212 
213     def __invert__(self):
214         return jax_frontend.lax.bitwise_not(self)
215 
216     def __lshift__(self, other):
217         return jax_frontend.lax.shift_left(self, other)
218 
219     def __rlshift__(self, other):
220         return jax_frontend.lax.shift_left(other, self)
221 
222     def __rshift__(self, other):
223         return jax_frontend.lax.shift_right_logical(self, other)
224 
225     def __rrshift__(self, other):
226         return jax_frontend.lax.shift_right_logical(other, self)
227 
228     def __getitem__(self, idx):
229         return self.at[idx].get()
230 
231     def __setitem__(self, idx, val):
232         raise ivy.utils.exceptions.IvyException(
233             "ivy.functional.frontends.jax.DeviceArray object doesn't support assignment"
234         )
235 
236     def __iter__(self):
237         ndim = len(self.shape)
238         if ndim == 0:
239             raise TypeError("iteration over a 0-d devicearray not supported")
240         for i in range(self.shape[0]):
241             yield self[i]
242 
243     def round(self, decimals=0):
244         return jax_frontend.numpy.round(self, decimals)
245
```
--- 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/devicearray.py b/ivy/functional/frontends/jax/devicearray.py
--- a/ivy/functional/frontends/jax/devicearray.py
+++ b/ivy/functional/frontends/jax/devicearray.py
@@ -41,6 +41,10 @@
     def at(self):
         return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)
 
+    @property
+    def T(self):
+        return self.ivy_array.T
+
     # Instance Methods #
     # ---------------- #

{"golden_diff": "diff --git a/ivy/functional/frontends/jax/devicearray.py b/ivy/functional/frontends/jax/devicearray.py\n--- a/ivy/functional/frontends/jax/devicearray.py\n+++ b/ivy/functional/frontends/jax/devicearray.py\n@@ -41,6 +41,10 @@\n     def at(self):\n         return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)\n \n+    @property\n+    def T(self):\n+        return self.ivy_array.T\n+\n     # Instance Methods #\n     # ---------------- #\n", "issue": "T\n\n", "before_files": [{"content": "# global\n\n# local\nimport ivy\nimport ivy.functional.frontends.jax as jax_frontend\n\n\nclass DeviceArray:\n    def __init__(self, array, weak_type=False):\n        self._ivy_array = array if isinstance(array, ivy.Array) else ivy.array(array)\n        self.weak_type = weak_type\n\n    def __repr__(self):\n        main = (\n            str(self.ivy_array.__repr__())\n            .replace(\"ivy.array\", \"ivy.frontends.jax.DeviceArray\")\n            .replace(\")\", \"\")\n            + \", dtype=\"\n            + str(self.ivy_array.dtype)\n        )\n        if self.weak_type:\n            return main + \", weak_type=True)\"\n        return main + \")\"\n\n    # Properties #\n    # ---------- #\n\n    @property\n    def ivy_array(self):\n        return self._ivy_array\n\n    @property\n    def dtype(self):\n        return self.ivy_array.dtype\n\n    @property\n    def shape(self):\n        return self.ivy_array.shape\n\n    @property\n    def at(self):\n        return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)\n\n    # Instance Methods #\n    # ---------------- #\n\n    def all(self, *, axis=None, out=None, keepdims=False):\n        return jax_frontend.numpy.all(\n            self._ivy_array, axis=axis, keepdims=keepdims, out=out\n        )\n\n    def argmax(\n        self,\n        /,\n        *,\n        axis=None,\n        out=None,\n        keepdims=False,\n    ):\n        return jax_frontend.numpy.argmax(\n            self,\n            axis=axis,\n            out=out,\n            keepdims=keepdims,\n        )\n\n    def conj(self, /):\n        return jax_frontend.numpy.conj(self._ivy_array)\n\n    def conjugate(self, /):\n        return jax_frontend.numpy.conjugate(self._ivy_array)\n\n    def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, where=None):\n        return jax_frontend.numpy.mean(\n            self._ivy_array,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n            keepdims=keepdims,\n            where=where,\n        )\n\n    def cumprod(self, axis=None, dtype=None, out=None):\n        return jax_frontend.numpy.cumprod(\n            self,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n        )\n\n    def cumsum(self, axis=None, dtype=None, out=None):\n        return jax_frontend.numpy.cumsum(\n            self,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n        )\n\n    def nonzero(self, *, size=None, fill_value=None):\n        return jax_frontend.numpy.nonzero(\n            self,\n            size=size,\n            fill_value=fill_value,\n        )\n\n    def ravel(self, order=\"C\"):\n        return jax_frontend.numpy.ravel(\n            self,\n            order=order,\n        )\n\n    def sort(self, axis=-1, order=None):\n        return jax_frontend.numpy.sort(\n            self,\n            axis=axis,\n            order=order,\n        )\n\n    def __add__(self, other):\n        return jax_frontend.numpy.add(self, other)\n\n    def __radd__(self, other):\n        return jax_frontend.numpy.add(other, self)\n\n    def __sub__(self, other):\n        return jax_frontend.lax.sub(self, other)\n\n    def __rsub__(self, other):\n        return jax_frontend.lax.sub(other, self)\n\n    def __mul__(self, other):\n        return jax_frontend.lax.mul(self, other)\n\n    def __rmul__(self, other):\n        return jax_frontend.lax.mul(other, self)\n\n    def __div__(self, other):\n        return jax_frontend.numpy.divide(self, other)\n\n    def __rdiv__(self, other):\n        return jax_frontend.numpy.divide(other, self)\n\n    def __mod__(self, other):\n        return jax_frontend.numpy.mod(self, other)\n\n    def __rmod__(self, other):\n        return jax_frontend.numpy.mod(other, self)\n\n    def __truediv__(self, other):\n        return jax_frontend.numpy.divide(self, other)\n\n    def __rtruediv__(self, other):\n        return jax_frontend.numpy.divide(other, self)\n\n    def __matmul__(self, other):\n        return jax_frontend.numpy.dot(self, other)\n\n    def __rmatmul__(self, other):\n        return jax_frontend.numpy.dot(other, self)\n\n    def __pos__(self):\n        return self\n\n    def __neg__(self):\n        return jax_frontend.lax.neg(self)\n\n    def __eq__(self, other):\n        return jax_frontend.lax.eq(self, other)\n\n    def __ne__(self, other):\n        return jax_frontend.lax.ne(self, other)\n\n    def __lt__(self, other):\n        return jax_frontend.lax.lt(self, other)\n\n    def __le__(self, other):\n        return jax_frontend.lax.le(self, other)\n\n    def __gt__(self, other):\n        return jax_frontend.lax.gt(self, other)\n\n    def __ge__(self, other):\n        return jax_frontend.lax.ge(self, other)\n\n    def __abs__(self):\n        return jax_frontend.numpy.abs(self)\n\n    def __pow__(self, other):\n        return jax_frontend.lax.pow(self, other)\n\n    def __rpow__(self, other):\n        other = ivy.asarray(other)\n        return jax_frontend.lax.pow(other, self)\n\n    def __and__(self, other):\n        return jax_frontend.numpy.bitwise_and(self, other)\n\n    def __rand__(self, other):\n        return jax_frontend.numpy.bitwise_and(other, self)\n\n    def __or__(self, other):\n        return jax_frontend.numpy.bitwise_or(self, other)\n\n    def __ror__(self, other):\n        return jax_frontend.numpy.bitwise_or(other, self)\n\n    def __xor__(self, other):\n        return jax_frontend.lax.bitwise_xor(self, other)\n\n    def __rxor__(self, other):\n        return jax_frontend.lax.bitwise_xor(other, self)\n\n    def __invert__(self):\n        return jax_frontend.lax.bitwise_not(self)\n\n    def __lshift__(self, other):\n        return jax_frontend.lax.shift_left(self, other)\n\n    def __rlshift__(self, other):\n        return jax_frontend.lax.shift_left(other, self)\n\n    def __rshift__(self, other):\n        return jax_frontend.lax.shift_right_logical(self, other)\n\n    def __rrshift__(self, other):\n        return jax_frontend.lax.shift_right_logical(other, self)\n\n    def __getitem__(self, idx):\n        return self.at[idx].get()\n\n    def __setitem__(self, idx, val):\n        raise ivy.utils.exceptions.IvyException(\n            \"ivy.functional.frontends.jax.DeviceArray object doesn't support assignment\"\n        )\n\n    def __iter__(self):\n        ndim = len(self.shape)\n        if ndim == 0:\n            raise TypeError(\"iteration over a 0-d devicearray not supported\")\n        for i in range(self.shape[0]):\n            yield self[i]\n\n    def round(self, decimals=0):\n        return jax_frontend.numpy.round(self, decimals)\n", "path": "ivy/functional/frontends/jax/devicearray.py"}], "after_files": [{"content": "# global\n\n# local\nimport ivy\nimport ivy.functional.frontends.jax as jax_frontend\n\n\nclass DeviceArray:\n    def __init__(self, array, weak_type=False):\n        self._ivy_array = array if isinstance(array, ivy.Array) else ivy.array(array)\n        self.weak_type = weak_type\n\n    def __repr__(self):\n        main = (\n            str(self.ivy_array.__repr__())\n            .replace(\"ivy.array\", \"ivy.frontends.jax.DeviceArray\")\n            .replace(\")\", \"\")\n            + \", dtype=\"\n            + str(self.ivy_array.dtype)\n        )\n        if self.weak_type:\n            return main + \", weak_type=True)\"\n        return main + \")\"\n\n    # Properties #\n    # ---------- #\n\n    @property\n    def ivy_array(self):\n        return self._ivy_array\n\n    @property\n    def dtype(self):\n        return self.ivy_array.dtype\n\n    @property\n    def shape(self):\n        return self.ivy_array.shape\n\n    @property\n    def at(self):\n        return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)\n\n    @property\n    def T(self):\n        return self.ivy_array.T\n\n    # Instance Methods #\n    # ---------------- #\n\n    def all(self, *, axis=None, out=None, keepdims=False):\n        return jax_frontend.numpy.all(\n            self._ivy_array, axis=axis, keepdims=keepdims, out=out\n        )\n\n    def argmax(\n        self,\n        /,\n        *,\n        axis=None,\n        out=None,\n        keepdims=False,\n    ):\n        return jax_frontend.numpy.argmax(\n            self,\n            axis=axis,\n            out=out,\n            keepdims=keepdims,\n        )\n\n    def conj(self, /):\n        return jax_frontend.numpy.conj(self._ivy_array)\n\n    def conjugate(self, /):\n        return jax_frontend.numpy.conjugate(self._ivy_array)\n\n    def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, where=None):\n        return jax_frontend.numpy.mean(\n            self._ivy_array,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n            keepdims=keepdims,\n            where=where,\n        )\n\n    def cumprod(self, axis=None, dtype=None, out=None):\n        return jax_frontend.numpy.cumprod(\n            self,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n        )\n\n    def cumsum(self, axis=None, dtype=None, out=None):\n        return jax_frontend.numpy.cumsum(\n            self,\n            axis=axis,\n            dtype=dtype,\n            out=out,\n        )\n\n    def nonzero(self, *, size=None, fill_value=None):\n        return jax_frontend.numpy.nonzero(\n            self,\n            size=size,\n            fill_value=fill_value,\n        )\n\n    def ravel(self, order=\"C\"):\n        return jax_frontend.numpy.ravel(\n            self,\n            order=order,\n        )\n\n    def sort(self, axis=-1, order=None):\n        return jax_frontend.numpy.sort(\n            self,\n            axis=axis,\n            order=order,\n        )\n\n    def __add__(self, other):\n        return jax_frontend.numpy.add(self, other)\n\n    def __radd__(self, other):\n        return jax_frontend.numpy.add(other, self)\n\n    def __sub__(self, other):\n        return jax_frontend.lax.sub(self, other)\n\n    def __rsub__(self, other):\n        return jax_frontend.lax.sub(other, self)\n\n    def __mul__(self, other):\n        return jax_frontend.lax.mul(self, other)\n\n    def __rmul__(self, other):\n        return jax_frontend.lax.mul(other, self)\n\n    def __div__(self, other):\n        return jax_frontend.numpy.divide(self, other)\n\n    def __rdiv__(self, other):\n        return jax_frontend.numpy.divide(other, self)\n\n    def __mod__(self, other):\n        return jax_frontend.numpy.mod(self, other)\n\n    def __rmod__(self, other):\n        return jax_frontend.numpy.mod(other, self)\n\n    def __truediv__(self, other):\n        return jax_frontend.numpy.divide(self, other)\n\n    def __rtruediv__(self, other):\n        return jax_frontend.numpy.divide(other, self)\n\n    def __matmul__(self, other):\n        return jax_frontend.numpy.dot(self, other)\n\n    def __rmatmul__(self, other):\n        return jax_frontend.numpy.dot(other, self)\n\n    def __pos__(self):\n        return self\n\n    def __neg__(self):\n        return jax_frontend.lax.neg(self)\n\n    def __eq__(self, other):\n        return jax_frontend.lax.eq(self, other)\n\n    def __ne__(self, other):\n        return jax_frontend.lax.ne(self, other)\n\n    def __lt__(self, other):\n        return jax_frontend.lax.lt(self, other)\n\n    def __le__(self, other):\n        return jax_frontend.lax.le(self, other)\n\n    def __gt__(self, other):\n        return jax_frontend.lax.gt(self, other)\n\n    def __ge__(self, other):\n        return jax_frontend.lax.ge(self, other)\n\n    def __abs__(self):\n        return jax_frontend.numpy.abs(self)\n\n    def __pow__(self, other):\n        return jax_frontend.lax.pow(self, other)\n\n    def __rpow__(self, other):\n        other = ivy.asarray(other)\n        return jax_frontend.lax.pow(other, self)\n\n    def __and__(self, other):\n        return jax_frontend.numpy.bitwise_and(self, other)\n\n    def __rand__(self, other):\n        return jax_frontend.numpy.bitwise_and(other, self)\n\n    def __or__(self, other):\n        return jax_frontend.numpy.bitwise_or(self, other)\n\n    def __ror__(self, other):\n        return jax_frontend.numpy.bitwise_or(other, self)\n\n    def __xor__(self, other):\n        return jax_frontend.lax.bitwise_xor(self, other)\n\n    def __rxor__(self, other):\n        return jax_frontend.lax.bitwise_xor(other, self)\n\n    def __invert__(self):\n        return jax_frontend.lax.bitwise_not(self)\n\n    def __lshift__(self, other):\n        return jax_frontend.lax.shift_left(self, other)\n\n    def __rlshift__(self, other):\n        return jax_frontend.lax.shift_left(other, self)\n\n    def __rshift__(self, other):\n        return jax_frontend.lax.shift_right_logical(self, other)\n\n    def __rrshift__(self, other):\n        return jax_frontend.lax.shift_right_logical(other, self)\n\n    def __getitem__(self, idx):\n        return self.at[idx].get()\n\n    def __setitem__(self, idx, val):\n        raise ivy.utils.exceptions.IvyException(\n            \"ivy.functional.frontends.jax.DeviceArray object doesn't support assignment\"\n        )\n\n    def __iter__(self):\n        ndim = len(self.shape)\n        if ndim == 0:\n            raise TypeError(\"iteration over a 0-d devicearray not supported\")\n        for i in range(self.shape[0]):\n            yield self[i]\n\n    def round(self, decimals=0):\n        return jax_frontend.numpy.round(self, decimals)\n", "path": "ivy/functional/frontends/jax/devicearray.py"}]}