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


--- 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/random.py`
Content:
```
1 # local
2 import ivy
3 from ivy.func_wrapper import with_unsupported_dtypes
4 from ivy.functional.frontends.jax.func_wrapper import (
5     to_ivy_arrays_and_back,
6     handle_jax_dtype,
7 )
8 
9 
10 @to_ivy_arrays_and_back
11 def PRNGKey(seed):
12     return ivy.array([0, seed % 4294967295 - (seed // 4294967295)], dtype=ivy.int64)
13 
14 
15 @handle_jax_dtype
16 @to_ivy_arrays_and_back
17 def uniform(key, shape=(), dtype=None, minval=0.0, maxval=1.0):
18     return ivy.random_uniform(
19         low=minval, high=maxval, shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1])
20     )
21 
22 
23 @handle_jax_dtype
24 @to_ivy_arrays_and_back
25 def normal(key, shape=(), dtype=None):
26     return ivy.random_normal(shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1]))
27 
28 
29 def _get_seed(key):
30     key1, key2 = int(key[0]), int(key[1])
31     return ivy.to_scalar(int("".join(map(str, [key1, key2]))))
32 
33 
34 @handle_jax_dtype
35 @to_ivy_arrays_and_back
36 @with_unsupported_dtypes(
37     {
38         "0.3.14 and below": (
39             "float16",
40             "bfloat16",
41         )
42     },
43     "jax",
44 )
45 def beta(key, a, b, shape=None, dtype=None):
46     seed = _get_seed(key)
47     return ivy.beta(a, b, shape=shape, dtype=dtype, seed=seed)
48 
49 
50 @handle_jax_dtype
51 @to_ivy_arrays_and_back
52 @with_unsupported_dtypes(
53     {
54         "0.3.14 and below": (
55             "float16",
56             "bfloat16",
57         )
58     },
59     "jax",
60 )
61 def dirichlet(key, alpha, shape=None, dtype="float32"):
62     seed = _get_seed(key)
63     alpha = ivy.astype(alpha, dtype)
64     return ivy.dirichlet(alpha, size=shape, dtype=dtype, seed=seed)
65 
66 
67 @handle_jax_dtype
68 @to_ivy_arrays_and_back
69 def cauchy(key, shape=(), dtype="float64"):
70     seed = _get_seed(key)
71     u = ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=seed)
72     return ivy.tan(ivy.pi * (u - 0.5))
73 
74 
75 @handle_jax_dtype
76 @to_ivy_arrays_and_back
77 @with_unsupported_dtypes(
78     {"0.3.14 and below": ("unsigned", "int8", "int16")},
79     "jax",
80 )
81 def poisson(key, lam, shape=None, dtype=None):
82     seed = _get_seed(key)
83     return ivy.poisson(lam, shape=shape, dtype=dtype, seed=seed)
84 
85 
86 @handle_jax_dtype
87 @to_ivy_arrays_and_back
88 @with_unsupported_dtypes(
89     {
90         "0.3.14 and below": (
91             "float16",
92             "bfloat16",
93         )
94     },
95     "jax",
96 )
97 def gamma(key, a, shape=None, dtype="float64"):
98     seed = _get_seed(key)
99     return ivy.gamma(a, 1.0, shape=shape, dtype=dtype, seed=seed)
100 
101 
102 @handle_jax_dtype
103 @to_ivy_arrays_and_back
104 @with_unsupported_dtypes(
105     {
106         "0.3.14 and below": (
107             "float16",
108             "bfloat16",
109         )
110     },
111     "jax",
112 )
113 def gumbel(key, shape=(), dtype="float64"):
114     seed = _get_seed(key)
115     uniform_x = ivy.random_uniform(
116         low=0.0,
117         high=1.0,
118         shape=shape,
119         dtype=dtype,
120         seed=seed,
121     )
122     return -ivy.log(-ivy.log(uniform_x))
123 
124 
125 @handle_jax_dtype
126 @to_ivy_arrays_and_back
127 @with_unsupported_dtypes(
128     {"0.3.14 and below": ("unsigned", "int8", "int16")},
129     "jax",
130 )
131 def rademacher(key, shape, dtype="int64"):
132     seed = _get_seed(key)
133     b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype="float32", seed=seed)
134     b = ivy.astype(b, dtype)
135     return 2 * b - 1
136 
137 
138 @handle_jax_dtype
139 @to_ivy_arrays_and_back
140 @with_unsupported_dtypes(
141     {
142         "0.3.14 and below": (
143             "float16",
144             "bfloat16",
145         )
146     },
147     "jax",
148 )
149 def generalized_normal(key, p, shape=(), dtype="float64"):
150     seed = _get_seed(key)
151     g = ivy.gamma(1 / p, 1.0, shape=shape, dtype=dtype, seed=seed)
152     b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=dtype, seed=seed)
153     r = 2 * b - 1
154     return r * g ** (1 / p)
155 
156 
157 def t(key, df, shape=(), dtype="float64"):
158     seed = _get_seed(key)
159     n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)
160     half_df = df / 2.0
161     g = ivy.gamma(half_df, 1.0, shape=shape, dtype=dtype, seed=seed)
162     return n * ivy.sqrt(ivy.divide(half_df, g))
163 
164 
165 @handle_jax_dtype
166 @to_ivy_arrays_and_back
167 @with_unsupported_dtypes(
168     {"0.3.14 and below": ("unsigned", "int8", "int16")},
169     "jax",
170 )
171 def randint(key, shape, minval, maxval, dtype="int64"):
172     seed = _get_seed(key)
173     return ivy.randint(minval, maxval, shape=shape, dtype=dtype, seed=seed)
174 
175 
176 @to_ivy_arrays_and_back
177 def permutation(key, x, axis=0, independent=False):
178     x = ivy.array(x)
179     seed = _get_seed(key)
180     if not ivy.get_num_dims(x):
181         r = int(x)
182         return ivy.shuffle(ivy.arange(r), axis, seed=seed)
183     if independent:
184         return ivy.shuffle(x, axis, seed=seed)
185     rand = ivy.arange(x.shape[axis])
186     ind = ivy.shuffle(rand, 0, seed=seed)
187 
188     return ivy.gather(x, ind, axis=axis)
189
```
--- 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/random.py b/ivy/functional/frontends/jax/random.py
--- a/ivy/functional/frontends/jax/random.py
+++ b/ivy/functional/frontends/jax/random.py
@@ -172,6 +172,17 @@
     seed = _get_seed(key)
     return ivy.randint(minval, maxval, shape=shape, dtype=dtype, seed=seed)
 
+@to_ivy_arrays_and_back
+def bernoulli(key, p=0.5, shape=None):
+    seed = _get_seed(key)
+    return ivy.bernoulli(p, shape=shape, seed=seed)
+
+@to_ivy_arrays_and_back
+def fold_in(key, data):
+    s = ivy.bitwise_left_shift(
+        ivy.asarray(data, dtype=ivy.uint32), ivy.array(32, dtype=ivy.uint32)
+    )
+    return ivy.bitwise_xor(key, s)
 
 @to_ivy_arrays_and_back
 def permutation(key, x, axis=0, independent=False):
@@ -184,5 +195,4 @@
         return ivy.shuffle(x, axis, seed=seed)
     rand = ivy.arange(x.shape[axis])
     ind = ivy.shuffle(rand, 0, seed=seed)
-
     return ivy.gather(x, ind, axis=axis)

{"golden_diff": "diff --git a/ivy/functional/frontends/jax/random.py b/ivy/functional/frontends/jax/random.py\n--- a/ivy/functional/frontends/jax/random.py\n+++ b/ivy/functional/frontends/jax/random.py\n@@ -172,6 +172,17 @@\n     seed = _get_seed(key)\n     return ivy.randint(minval, maxval, shape=shape, dtype=dtype, seed=seed)\n \n+@to_ivy_arrays_and_back\n+def bernoulli(key, p=0.5, shape=None):\n+    seed = _get_seed(key)\n+    return ivy.bernoulli(p, shape=shape, seed=seed)\n+\n+@to_ivy_arrays_and_back\n+def fold_in(key, data):\n+    s = ivy.bitwise_left_shift(\n+        ivy.asarray(data, dtype=ivy.uint32), ivy.array(32, dtype=ivy.uint32)\n+    )\n+    return ivy.bitwise_xor(key, s)\n \n @to_ivy_arrays_and_back\n def permutation(key, x, axis=0, independent=False):\n@@ -184,5 +195,4 @@\n         return ivy.shuffle(x, axis, seed=seed)\n     rand = ivy.arange(x.shape[axis])\n     ind = ivy.shuffle(rand, 0, seed=seed)\n-\n     return ivy.gather(x, ind, axis=axis)\n", "issue": "fold_in\n\n", "before_files": [{"content": "# local\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n    handle_jax_dtype,\n)\n\n\n@to_ivy_arrays_and_back\ndef PRNGKey(seed):\n    return ivy.array([0, seed % 4294967295 - (seed // 4294967295)], dtype=ivy.int64)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef uniform(key, shape=(), dtype=None, minval=0.0, maxval=1.0):\n    return ivy.random_uniform(\n        low=minval, high=maxval, shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1])\n    )\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef normal(key, shape=(), dtype=None):\n    return ivy.random_normal(shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1]))\n\n\ndef _get_seed(key):\n    key1, key2 = int(key[0]), int(key[1])\n    return ivy.to_scalar(int(\"\".join(map(str, [key1, key2]))))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef beta(key, a, b, shape=None, dtype=None):\n    seed = _get_seed(key)\n    return ivy.beta(a, b, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef dirichlet(key, alpha, shape=None, dtype=\"float32\"):\n    seed = _get_seed(key)\n    alpha = ivy.astype(alpha, dtype)\n    return ivy.dirichlet(alpha, size=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef cauchy(key, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    u = ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=seed)\n    return ivy.tan(ivy.pi * (u - 0.5))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef poisson(key, lam, shape=None, dtype=None):\n    seed = _get_seed(key)\n    return ivy.poisson(lam, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef gamma(key, a, shape=None, dtype=\"float64\"):\n    seed = _get_seed(key)\n    return ivy.gamma(a, 1.0, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef gumbel(key, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    uniform_x = ivy.random_uniform(\n        low=0.0,\n        high=1.0,\n        shape=shape,\n        dtype=dtype,\n        seed=seed,\n    )\n    return -ivy.log(-ivy.log(uniform_x))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef rademacher(key, shape, dtype=\"int64\"):\n    seed = _get_seed(key)\n    b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=\"float32\", seed=seed)\n    b = ivy.astype(b, dtype)\n    return 2 * b - 1\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef generalized_normal(key, p, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    g = ivy.gamma(1 / p, 1.0, shape=shape, dtype=dtype, seed=seed)\n    b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=dtype, seed=seed)\n    r = 2 * b - 1\n    return r * g ** (1 / p)\n\n\ndef t(key, df, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)\n    half_df = df / 2.0\n    g = ivy.gamma(half_df, 1.0, shape=shape, dtype=dtype, seed=seed)\n    return n * ivy.sqrt(ivy.divide(half_df, g))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef randint(key, shape, minval, maxval, dtype=\"int64\"):\n    seed = _get_seed(key)\n    return ivy.randint(minval, maxval, shape=shape, dtype=dtype, seed=seed)\n\n\n@to_ivy_arrays_and_back\ndef permutation(key, x, axis=0, independent=False):\n    x = ivy.array(x)\n    seed = _get_seed(key)\n    if not ivy.get_num_dims(x):\n        r = int(x)\n        return ivy.shuffle(ivy.arange(r), axis, seed=seed)\n    if independent:\n        return ivy.shuffle(x, axis, seed=seed)\n    rand = ivy.arange(x.shape[axis])\n    ind = ivy.shuffle(rand, 0, seed=seed)\n\n    return ivy.gather(x, ind, axis=axis)\n", "path": "ivy/functional/frontends/jax/random.py"}], "after_files": [{"content": "# local\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n    handle_jax_dtype,\n)\n\n\n@to_ivy_arrays_and_back\ndef PRNGKey(seed):\n    return ivy.array([0, seed % 4294967295 - (seed // 4294967295)], dtype=ivy.int64)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef uniform(key, shape=(), dtype=None, minval=0.0, maxval=1.0):\n    return ivy.random_uniform(\n        low=minval, high=maxval, shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1])\n    )\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef normal(key, shape=(), dtype=None):\n    return ivy.random_normal(shape=shape, dtype=dtype, seed=ivy.to_scalar(key[1]))\n\n\ndef _get_seed(key):\n    key1, key2 = int(key[0]), int(key[1])\n    return ivy.to_scalar(int(\"\".join(map(str, [key1, key2]))))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef beta(key, a, b, shape=None, dtype=None):\n    seed = _get_seed(key)\n    return ivy.beta(a, b, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef dirichlet(key, alpha, shape=None, dtype=\"float32\"):\n    seed = _get_seed(key)\n    alpha = ivy.astype(alpha, dtype)\n    return ivy.dirichlet(alpha, size=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef cauchy(key, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    u = ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=seed)\n    return ivy.tan(ivy.pi * (u - 0.5))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef poisson(key, lam, shape=None, dtype=None):\n    seed = _get_seed(key)\n    return ivy.poisson(lam, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef gamma(key, a, shape=None, dtype=\"float64\"):\n    seed = _get_seed(key)\n    return ivy.gamma(a, 1.0, shape=shape, dtype=dtype, seed=seed)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef gumbel(key, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    uniform_x = ivy.random_uniform(\n        low=0.0,\n        high=1.0,\n        shape=shape,\n        dtype=dtype,\n        seed=seed,\n    )\n    return -ivy.log(-ivy.log(uniform_x))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef rademacher(key, shape, dtype=\"int64\"):\n    seed = _get_seed(key)\n    b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=\"float32\", seed=seed)\n    b = ivy.astype(b, dtype)\n    return 2 * b - 1\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\n)\ndef generalized_normal(key, p, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    g = ivy.gamma(1 / p, 1.0, shape=shape, dtype=dtype, seed=seed)\n    b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=dtype, seed=seed)\n    r = 2 * b - 1\n    return r * g ** (1 / p)\n\n\ndef t(key, df, shape=(), dtype=\"float64\"):\n    seed = _get_seed(key)\n    n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)\n    half_df = df / 2.0\n    g = ivy.gamma(half_df, 1.0, shape=shape, dtype=dtype, seed=seed)\n    return n * ivy.sqrt(ivy.divide(half_df, g))\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\n@with_unsupported_dtypes(\n    {\"0.3.14 and below\": (\"unsigned\", \"int8\", \"int16\")},\n    \"jax\",\n)\ndef randint(key, shape, minval, maxval, dtype=\"int64\"):\n    seed = _get_seed(key)\n    return ivy.randint(minval, maxval, shape=shape, dtype=dtype, seed=seed)\n\n@to_ivy_arrays_and_back\ndef bernoulli(key, p=0.5, shape=None):\n    seed = _get_seed(key)\n    return ivy.bernoulli(p, shape=shape, seed=seed)\n\n@to_ivy_arrays_and_back\ndef fold_in(key, data):\n    s = ivy.bitwise_left_shift(\n        ivy.asarray(data, dtype=ivy.uint32), ivy.array(32, dtype=ivy.uint32)\n    )\n    return ivy.bitwise_xor(key, s)\n\n@to_ivy_arrays_and_back\ndef permutation(key, x, axis=0, independent=False):\n    x = ivy.array(x)\n    seed = _get_seed(key)\n    if not ivy.get_num_dims(x):\n        r = int(x)\n        return ivy.shuffle(ivy.arange(r), axis, seed=seed)\n    if independent:\n        return ivy.shuffle(x, axis, seed=seed)\n    rand = ivy.arange(x.shape[axis])\n    ind = ivy.shuffle(rand, 0, seed=seed)\n    return ivy.gather(x, ind, axis=axis)\n", "path": "ivy/functional/frontends/jax/random.py"}]}