prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  adaptive_avg_pool2d
  Close #20804
  
  --- 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/mindspore/ops/function/nn_func.py`
  Content:
  ```
  1 """Includes Mindspore Frontend functions listed in the TODO list
  2 https://github.com/unifyai/ivy/issues/14951."""
  3 
  4 # local
  5 import ivy
  6 from ivy.func_wrapper import with_supported_dtypes
  7 from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
  8 
  9 
  10 @with_supported_dtypes(
  11     {
  12         "2.0.0 and below": (
  13             "int8",
  14             "int16",
  15             "int32",
  16             "int64",
  17             "float16",
  18             "float32",
  19             "float64",
  20         )
  21     },
  22     "mindspore",
  23 )
  24 @to_ivy_arrays_and_back
  25 def dropout2d(input, p=0.5, training=True):
  26     return ivy.dropout2d(input, p, training=training, data_format="NCHW")
  27 
  28 
  29 @with_supported_dtypes({"2.0.0 and below": ("float16", "float32")}, "mindspore")
  30 @to_ivy_arrays_and_back
  31 def selu(input_x):
  32     return ivy.selu(input_x)
  33 
  34 
  35 @with_supported_dtypes({"2.0 and below": ("float16", "float32")}, "mindspore")
  36 @to_ivy_arrays_and_back
  37 def softsign(x):
  38     return ivy.divide(x, ivy.add(1, ivy.abs(x)))
  39 
  40 @with_supported_dtypes({"2.0 and below": ("int8", "int16", "int32", "int64", "float16", "float32", "float64")}, "mindspore")
  41 @to_ivy_arrays_and_back
  42 def pad(input, pad_width, mode='constant', constant_values=0):
  43     return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)
  ```
  --- 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/mindspore/ops/function/nn_func.py b/ivy/functional/frontends/mindspore/ops/function/nn_func.py
  --- a/ivy/functional/frontends/mindspore/ops/function/nn_func.py
  +++ b/ivy/functional/frontends/mindspore/ops/function/nn_func.py
  @@ -40,4 +40,10 @@
   @with_supported_dtypes({"2.0 and below": ("int8", "int16", "int32", "int64", "float16", "float32", "float64")}, "mindspore")
   @to_ivy_arrays_and_back
   def pad(input, pad_width, mode='constant', constant_values=0):
  -    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)
  \ No newline at end of file
  +    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)
  +
  +
  +@with_supported_dtypes({"2.0.0 and below": ("float16", "float32", "float64")}, "mindspore")
  +@to_ivy_arrays_and_back
  +def adaptive_avg_pool2d(input, output_size):
  +    return ivy.adaptive_avg_pool2d(input, output_size)
  
verification_info: {"golden_diff": "diff --git a/ivy/functional/frontends/mindspore/ops/function/nn_func.py b/ivy/functional/frontends/mindspore/ops/function/nn_func.py\n--- a/ivy/functional/frontends/mindspore/ops/function/nn_func.py\n+++ b/ivy/functional/frontends/mindspore/ops/function/nn_func.py\n@@ -40,4 +40,10 @@\n @with_supported_dtypes({\"2.0 and below\": (\"int8\", \"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")}, \"mindspore\")\n @to_ivy_arrays_and_back\n def pad(input, pad_width, mode='constant', constant_values=0):\n-    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)\n\\ No newline at end of file\n+    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)\n+\n+\n+@with_supported_dtypes({\"2.0.0 and below\": (\"float16\", \"float32\", \"float64\")}, \"mindspore\")\n+@to_ivy_arrays_and_back\n+def adaptive_avg_pool2d(input, output_size):\n+    return ivy.adaptive_avg_pool2d(input, output_size)\n", "issue": "adaptive_avg_pool2d\nClose #20804\n", "before_files": [{"content": "\"\"\"Includes Mindspore Frontend functions listed in the TODO list\nhttps://github.com/unifyai/ivy/issues/14951.\"\"\"\n\n# local\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back\n\n\n@with_supported_dtypes(\n    {\n        \"2.0.0 and below\": (\n            \"int8\",\n            \"int16\",\n            \"int32\",\n            \"int64\",\n            \"float16\",\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"mindspore\",\n)\n@to_ivy_arrays_and_back\ndef dropout2d(input, p=0.5, training=True):\n    return ivy.dropout2d(input, p, training=training, data_format=\"NCHW\")\n\n\n@with_supported_dtypes({\"2.0.0 and below\": (\"float16\", \"float32\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef selu(input_x):\n    return ivy.selu(input_x)\n\n\n@with_supported_dtypes({\"2.0 and below\": (\"float16\", \"float32\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef softsign(x):\n    return ivy.divide(x, ivy.add(1, ivy.abs(x)))\n\n@with_supported_dtypes({\"2.0 and below\": (\"int8\", \"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef pad(input, pad_width, mode='constant', constant_values=0):\n    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)", "path": "ivy/functional/frontends/mindspore/ops/function/nn_func.py"}], "after_files": [{"content": "\"\"\"Includes Mindspore Frontend functions listed in the TODO list\nhttps://github.com/unifyai/ivy/issues/14951.\"\"\"\n\n# local\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back\n\n\n@with_supported_dtypes(\n    {\n        \"2.0.0 and below\": (\n            \"int8\",\n            \"int16\",\n            \"int32\",\n            \"int64\",\n            \"float16\",\n            \"float32\",\n            \"float64\",\n        )\n    },\n    \"mindspore\",\n)\n@to_ivy_arrays_and_back\ndef dropout2d(input, p=0.5, training=True):\n    return ivy.dropout2d(input, p, training=training, data_format=\"NCHW\")\n\n\n@with_supported_dtypes({\"2.0.0 and below\": (\"float16\", \"float32\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef selu(input_x):\n    return ivy.selu(input_x)\n\n\n@with_supported_dtypes({\"2.0 and below\": (\"float16\", \"float32\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef softsign(x):\n    return ivy.divide(x, ivy.add(1, ivy.abs(x)))\n\n@with_supported_dtypes({\"2.0 and below\": (\"int8\", \"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef pad(input, pad_width, mode='constant', constant_values=0):\n    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)\n\n\n@with_supported_dtypes({\"2.0.0 and below\": (\"float16\", \"float32\", \"float64\")}, \"mindspore\")\n@to_ivy_arrays_and_back\ndef adaptive_avg_pool2d(input, output_size):\n    return ivy.adaptive_avg_pool2d(input, output_size)\n", "path": "ivy/functional/frontends/mindspore/ops/function/nn_func.py"}]}