prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  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/torch/random_sampling.py`
  Content:
  ```
  1 import ivy
  2 from ivy.func_wrapper import with_supported_dtypes
  3 from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
  4 
  5 try:
  6     from torch import Generator
  7 except ImportError:
  8     from types import SimpleNamespace
  9 
  10     Generator = SimpleNamespace
  11 
  12 
  13 def seed() -> int:
  14     """Returns a 64 bit number used to seed the RNG"""
  15     return int(ivy.randint(-(2**63), 2**63 - 1))
  16 
  17 
  18 @to_ivy_arrays_and_back
  19 def manual_seed(seed: int):
  20     ivy.seed(seed_value=seed)
  21     return Generator().manual_seed(seed)
  22 
  23 
  24 @with_supported_dtypes(
  25     {
  26         "1.11.0 and below": (
  27             "float32",
  28             "float64",
  29         )
  30     },
  31     "torch",
  32 )
  33 @to_ivy_arrays_and_back
  34 def multinomial(input, num_samples, replacement=False, *, generator=None, out=None):
  35     return ivy.multinomial(
  36         num_samples + 1,  # doesn't matter because `probs` is provided, but should be
  37         # greater than the number of samples
  38         num_samples,
  39         probs=input,
  40         replace=replacement,
  41         out=out,
  42     )
  43 
  44 
  45 @with_supported_dtypes(
  46     {
  47         "1.11.0 and below": (
  48             "float32",
  49             "float64",
  50         )
  51     },
  52     "torch",
  53 )
  54 @to_ivy_arrays_and_back
  55 def poisson(input, generator=None):
  56     return ivy.poisson(input, shape=None)
  57 
  58 
  59 @to_ivy_arrays_and_back
  60 def rand(
  61     size,
  62     *,
  63     generator=None,
  64     out=None,
  65     dtype=None,
  66     layout=None,
  67     device=None,
  68     requires_grad=False,
  69     pin_memory=False
  70 ):
  71     return ivy.random_uniform(
  72         shape=size,
  73         out=out,
  74         dtype=dtype,
  75         device=device,
  76     )
  77 
  78 
  79 @to_ivy_arrays_and_back
  80 def rand_like(
  81     input,
  82     *,
  83     dtype=None,
  84     layout=None,
  85     device=None,
  86     requires_grad=False,
  87     memory_format=False
  88 ):
  89     shape = input.shape
  90     if not dtype:
  91         dtype = input.dtype
  92 
  93     return ivy.random_uniform(
  94         shape=shape,
  95         dtype=dtype,
  96         device=device,
  97     )
  98 
  99 
  100 @to_ivy_arrays_and_back
  101 def randn(
  102     size,
  103     *,
  104     generator=None,
  105     out=None,
  106     dtype=None,
  107     layout=None,
  108     device=None,
  109     requires_grad=False,
  110     pin_memory=False
  111 ):
  112     return ivy.random_normal(
  113         shape=size,
  114         out=out,
  115         dtype=dtype,
  116         device=device,
  117     )
  118
  ```
  --- 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/torch/random_sampling.py b/ivy/functional/frontends/torch/random_sampling.py
  --- a/ivy/functional/frontends/torch/random_sampling.py
  +++ b/ivy/functional/frontends/torch/random_sampling.py
  @@ -76,6 +76,20 @@
       )
   
   
  +@with_supported_dtypes(
  +    {
  +        "1.11.0 and below": (
  +            "float32",
  +            "float64",
  +        )
  +    },
  +    "torch",
  +)
  +@to_ivy_arrays_and_back
  +def normal(mean, std, *, generator=None, out=None):
  +    return ivy.random_normal(mean=mean, std=std, out=out)
  +    
  +
   @to_ivy_arrays_and_back
   def rand_like(
       input,
  
verification_info: {"golden_diff": "diff --git a/ivy/functional/frontends/torch/random_sampling.py b/ivy/functional/frontends/torch/random_sampling.py\n--- a/ivy/functional/frontends/torch/random_sampling.py\n+++ b/ivy/functional/frontends/torch/random_sampling.py\n@@ -76,6 +76,20 @@\n     )\n \n \n+@with_supported_dtypes(\n+    {\n+        \"1.11.0 and below\": (\n+            \"float32\",\n+            \"float64\",\n+        )\n+    },\n+    \"torch\",\n+)\n+@to_ivy_arrays_and_back\n+def normal(mean, std, *, generator=None, out=None):\n+    return ivy.random_normal(mean=mean, std=std, out=out)\n+    \n+\n @to_ivy_arrays_and_back\n def rand_like(\n     input,\n", "issue": "normal\n\n", "before_files": [{"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n    from torch import Generator\nexcept ImportError:\n    from types import SimpleNamespace\n\n    Generator = SimpleNamespace\n\n\ndef seed() -> int:\n    \"\"\"Returns a 64 bit number used to seed the RNG\"\"\"\n    return int(ivy.randint(-(2**63), 2**63 - 1))\n\n\n@to_ivy_arrays_and_back\ndef manual_seed(seed: int):\n    ivy.seed(seed_value=seed)\n    return Generator().manual_seed(seed)\n\n\n@with_supported_dtypes(\n    {\n        \"1.11.0 and below\": (\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"torch\",\n)\n@to_ivy_arrays_and_back\ndef multinomial(input, num_samples, replacement=False, *, generator=None, out=None):\n    return ivy.multinomial(\n        num_samples + 1,  # doesn't matter because `probs` is provided, but should be\n        # greater than the number of samples\n        num_samples,\n        probs=input,\n        replace=replacement,\n        out=out,\n    )\n\n\n@with_supported_dtypes(\n    {\n        \"1.11.0 and below\": (\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"torch\",\n)\n@to_ivy_arrays_and_back\ndef poisson(input, generator=None):\n    return ivy.poisson(input, shape=None)\n\n\n@to_ivy_arrays_and_back\ndef rand(\n    size,\n    *,\n    generator=None,\n    out=None,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    pin_memory=False\n):\n    return ivy.random_uniform(\n        shape=size,\n        out=out,\n        dtype=dtype,\n        device=device,\n    )\n\n\n@to_ivy_arrays_and_back\ndef rand_like(\n    input,\n    *,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    memory_format=False\n):\n    shape = input.shape\n    if not dtype:\n        dtype = input.dtype\n\n    return ivy.random_uniform(\n        shape=shape,\n        dtype=dtype,\n        device=device,\n    )\n\n\n@to_ivy_arrays_and_back\ndef randn(\n    size,\n    *,\n    generator=None,\n    out=None,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    pin_memory=False\n):\n    return ivy.random_normal(\n        shape=size,\n        out=out,\n        dtype=dtype,\n        device=device,\n    )\n", "path": "ivy/functional/frontends/torch/random_sampling.py"}], "after_files": [{"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n    from torch import Generator\nexcept ImportError:\n    from types import SimpleNamespace\n\n    Generator = SimpleNamespace\n\n\ndef seed() -> int:\n    \"\"\"Returns a 64 bit number used to seed the RNG\"\"\"\n    return int(ivy.randint(-(2**63), 2**63 - 1))\n\n\n@to_ivy_arrays_and_back\ndef manual_seed(seed: int):\n    ivy.seed(seed_value=seed)\n    return Generator().manual_seed(seed)\n\n\n@with_supported_dtypes(\n    {\n        \"1.11.0 and below\": (\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"torch\",\n)\n@to_ivy_arrays_and_back\ndef multinomial(input, num_samples, replacement=False, *, generator=None, out=None):\n    return ivy.multinomial(\n        num_samples + 1,  # doesn't matter because `probs` is provided, but should be\n        # greater than the number of samples\n        num_samples,\n        probs=input,\n        replace=replacement,\n        out=out,\n    )\n\n\n@with_supported_dtypes(\n    {\n        \"1.11.0 and below\": (\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"torch\",\n)\n@to_ivy_arrays_and_back\ndef poisson(input, generator=None):\n    return ivy.poisson(input, shape=None)\n\n\n@to_ivy_arrays_and_back\ndef rand(\n    size,\n    *,\n    generator=None,\n    out=None,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    pin_memory=False\n):\n    return ivy.random_uniform(\n        shape=size,\n        out=out,\n        dtype=dtype,\n        device=device,\n    )\n\n\n@with_supported_dtypes(\n    {\n        \"1.11.0 and below\": (\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"torch\",\n)\n@to_ivy_arrays_and_back\ndef normal(mean, std, *, generator=None, out=None):\n    return ivy.random_normal(mean=mean, std=std, out=out)\n    \n\n@to_ivy_arrays_and_back\ndef rand_like(\n    input,\n    *,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    memory_format=False\n):\n    shape = input.shape\n    if not dtype:\n        dtype = input.dtype\n\n    return ivy.random_uniform(\n        shape=shape,\n        dtype=dtype,\n        device=device,\n    )\n\n\n@to_ivy_arrays_and_back\ndef randn(\n    size,\n    *,\n    generator=None,\n    out=None,\n    dtype=None,\n    layout=None,\n    device=None,\n    requires_grad=False,\n    pin_memory=False\n):\n    return ivy.random_normal(\n        shape=size,\n        out=out,\n        dtype=dtype,\n        device=device,\n    )\n", "path": "ivy/functional/frontends/torch/random_sampling.py"}]}