prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  generalized_normal
  
  
  --- 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 @with_unsupported_dtypes(
  70     {"0.3.14 and below": ("unsigned", "int8", "int16")},
  71     "jax",
  72 )
  73 def poisson(key, lam, shape=None, dtype=None):
  74     seed = _get_seed(key)
  75     return ivy.poisson(lam, shape=shape, dtype=dtype, seed=seed)
  76 
  77 
  78 @handle_jax_dtype
  79 @to_ivy_arrays_and_back
  80 @with_unsupported_dtypes(
  81     {
  82         "0.3.14 and below": (
  83             "float16",
  84             "bfloat16",
  85         )
  86     },
  87     "jax",
  88 )
  89 def gamma(key, a, shape=None, dtype="float64"):
  90     seed = _get_seed(key)
  91     return ivy.gamma(a, 1.0, shape=shape, dtype=dtype, seed=seed)
  92 
  93 
  94 @handle_jax_dtype
  95 @to_ivy_arrays_and_back
  96 @with_unsupported_dtypes(
  97     {
  98         "0.3.14 and below": (
  99             "float16",
  100             "bfloat16",
  101         )
  102     },
  103     "jax",
  104 )
  105 def gumbel(key, shape=(), dtype="float64"):
  106     seed = _get_seed(key)
  107     uniform_x = ivy.random_uniform(
  108         low=0.0,
  109         high=1.0,
  110         shape=shape,
  111         dtype=dtype,
  112         seed=seed,
  113     )
  114     return -ivy.log(-ivy.log(uniform_x))
  115 
  116 
  117 @handle_jax_dtype
  118 @to_ivy_arrays_and_back
  119 @with_unsupported_dtypes(
  120     {
  121         "0.3.14 and below": (
  122             "float16",
  123             "bfloat16",
  124         )
  125     },
  126     "jax",
  127 )
  128 def t(key, df, shape=(), dtype="float64"):
  129     seed = _get_seed(key)
  130     n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)
  131     half_df = df / 2.0
  132     g = ivy.gamma(half_df, 1.0, shape=shape, dtype=dtype, seed=seed)
  133     return n * ivy.sqrt(ivy.divide(half_df, g))
  134
  ```
  --- 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()
  
  ```
  
golden_diff: 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
  @@ -125,6 +125,14 @@
       },
       "jax",
   )
  +def generalized_normal(key, p, shape=(), dtype="float64"):
  +    seed = _get_seed(key)
  +    g = ivy.gamma(1 / p, 1.0, shape=shape, dtype=dtype, seed=seed)
  +    b = ivy.bernoulli(ivy.array([0.5]), shape=shape, dtype=dtype, seed=seed)
  +    r = 2 * b - 1
  +    return r * g ** (1 / p)
  +
  +
   def t(key, df, shape=(), dtype="float64"):
       seed = _get_seed(key)
       n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)
  
verification_info: {"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@@ -125,6 +125,14 @@\n     },\n     \"jax\",\n )\n+def 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+\n def t(key, df, shape=(), dtype=\"float64\"):\n     seed = _get_seed(key)\n     n = ivy.random_normal(shape=shape, dtype=dtype, seed=seed)\n", "issue": "generalized_normal\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\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    {\n        \"0.3.14 and below\": (\n            \"float16\",\n            \"bfloat16\",\n        )\n    },\n    \"jax\",\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", "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\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    {\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", "path": "ivy/functional/frontends/jax/random.py"}]}