We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
NVTX annotation of multiple use of a transform
**Is your feature request related to a problem? Please describe.**
NVTX Range decorator currently is able to to patch each transform once. Multiple use of the same transforms with `Range` will result in duplicate annotations.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `monai/utils/nvtx.py`
Content:
```
1 # Copyright 2020 - 2021 MONAI Consortium
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at
5 #     http://www.apache.org/licenses/LICENSE-2.0
6 # Unless required by applicable law or agreed to in writing, software
7 # distributed under the License is distributed on an "AS IS" BASIS,
8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
9 # See the License for the specific language governing permissions and
10 # limitations under the License.
11 """
12 Decorators and context managers for NVIDIA Tools Extension to profile MONAI components
13 """
14 
15 from collections import defaultdict
16 from functools import wraps
17 from typing import Any, Optional, Tuple, Union
18 
19 from torch.autograd import Function
20 from torch.nn import Module
21 from torch.optim import Optimizer
22 from torch.utils.data import Dataset
23 
24 from monai.utils import ensure_tuple, optional_import
25 
26 _nvtx, _ = optional_import("torch._C._nvtx", descriptor="NVTX is not installed. Are you sure you have a CUDA build?")
27 
28 __all__ = ["Range"]
29 
30 
31 class Range:
32     """
33     A decorator and context manager for NVIDIA Tools Extension (NVTX) Range for profiling.
34     When used as a decorator it encloses a specific method of the object with an NVTX Range.
35     When used as a context manager, it encloses the runtime context (created by with statement) with an NVTX Range.
36 
37     Args:
38         name: the name to be associated to the range
39         methods: (only when used as decorator) the name of a method (or a list of the name of the methods)
40             to be wrapped by NVTX range.
41             If None (default), the method(s) will be inferred based on the object's type for various MONAI components,
42             such as Networks, Losses, Functions, Transforms, and Datasets.
43             Otherwise, it look up predefined methods: "forward", "__call__", "__next__", "__getitem__"
44         append_method_name: if append the name of the methods to be decorated to the range's name
45             If None (default), it appends the method's name only if we are annotating more than one method.
46 
47     """
48 
49     name_counter: dict = defaultdict(int)
50 
51     def __init__(
52         self,
53         name: Optional[str] = None,
54         methods: Optional[Union[str, Tuple[str, ...]]] = None,
55         append_method_name: Optional[bool] = None,
56     ) -> None:
57         self.name = name
58         self.methods = methods
59         self.append_method_name = append_method_name
60 
61     def __call__(self, obj: Any):
62         # Define the name to be associated to the range if not provided
63         if self.name is None:
64             name = type(obj).__name__
65             self.name_counter[name] += 1
66             self.name = f"{name}_{self.name_counter[name]}"
67 
68         # Define the methods to be wrapped if not provided
69         if self.methods is None:
70             self.methods = self._get_method(obj)
71         else:
72             self.methods = ensure_tuple(self.methods)
73 
74         # Check if to append method's name to the range's name
75         if self.append_method_name is None:
76             if len(self.methods) > 1:
77                 self.append_method_name = True
78             else:
79                 self.append_method_name = False
80 
81         # Decorate the methods
82         for method in self.methods:
83             self._decorate_method(obj, method, self.append_method_name)
84 
85         return obj
86 
87     def _decorate_method(self, obj, method, append_method_name):
88         # Append the method's name to the range's name
89         if append_method_name:
90             name = f"{self.name}.{method}"
91         else:
92             name = self.name
93 
94         # Get the class for special functions
95         if method.startswith("_"):
96             owner = type(obj)
97         else:
98             owner = obj
99 
100         # Get the method to be wrapped
101         _temp_func = getattr(owner, method)
102 
103         # Wrap the method with NVTX range (range push/pop)
104         @wraps(_temp_func)
105         def range_wrapper(*args, **kwargs):
106             _nvtx.rangePushA(name)
107             output = _temp_func(*args, **kwargs)
108             _nvtx.rangePop()
109             return output
110 
111         # Replace the method with the wrapped version
112         setattr(owner, method, range_wrapper)
113 
114     def _get_method(self, obj: Any) -> tuple:
115         if isinstance(obj, Module):
116             method_list = ["forward"]
117         elif isinstance(obj, Optimizer):
118             method_list = ["step"]
119         elif isinstance(obj, Function):
120             method_list = ["forward", "backward"]
121         elif isinstance(obj, Dataset):
122             method_list = ["__getitem__"]
123         else:
124             default_methods = ["forward", "__call__", "__next__", "__getitem__"]
125             method_list = []
126             for method in default_methods:
127                 if hasattr(obj, method):
128                     method_list.append(method)
129             if len(method_list) < 1:
130                 raise ValueError(
131                     f"The method to be wrapped for this object [{type(obj)}] is not recognized."
132                     "The name of the method should be provied or the object should have one of these methods:"
133                     f"{default_methods}"
134                 )
135         return ensure_tuple(method_list)
136 
137     def __enter__(self):
138         if self.name is None:
139             # Number the range with class variable counter to avoid duplicate names.
140             self.name_counter["context"] += 1
141             self.name = f"context_{self.name_counter['context']}"
142 
143         _nvtx.rangePushA(self.name)
144 
145     def __exit__(self, type, value, traceback):
146         _nvtx.rangePop()
147
```
--- 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/monai/utils/nvtx.py b/monai/utils/nvtx.py
--- a/monai/utils/nvtx.py
+++ b/monai/utils/nvtx.py
@@ -92,7 +92,7 @@
             name = self.name
 
         # Get the class for special functions
-        if method.startswith("_"):
+        if method.startswith("__"):
             owner = type(obj)
         else:
             owner = obj
@@ -109,7 +109,16 @@
             return output
 
         # Replace the method with the wrapped version
-        setattr(owner, method, range_wrapper)
+        if method.startswith("__"):
+            # If it is a special method, it requires special attention
+            class NVTXRangeDecoratedClass(owner):
+                ...
+
+            setattr(NVTXRangeDecoratedClass, method, range_wrapper)
+            obj.__class__ = NVTXRangeDecoratedClass
+
+        else:
+            setattr(owner, method, range_wrapper)
 
     def _get_method(self, obj: Any) -> tuple:
         if isinstance(obj, Module):

{"golden_diff": "diff --git a/monai/utils/nvtx.py b/monai/utils/nvtx.py\n--- a/monai/utils/nvtx.py\n+++ b/monai/utils/nvtx.py\n@@ -92,7 +92,7 @@\n             name = self.name\n \n         # Get the class for special functions\n-        if method.startswith(\"_\"):\n+        if method.startswith(\"__\"):\n             owner = type(obj)\n         else:\n             owner = obj\n@@ -109,7 +109,16 @@\n             return output\n \n         # Replace the method with the wrapped version\n-        setattr(owner, method, range_wrapper)\n+        if method.startswith(\"__\"):\n+            # If it is a special method, it requires special attention\n+            class NVTXRangeDecoratedClass(owner):\n+                ...\n+\n+            setattr(NVTXRangeDecoratedClass, method, range_wrapper)\n+            obj.__class__ = NVTXRangeDecoratedClass\n+\n+        else:\n+            setattr(owner, method, range_wrapper)\n \n     def _get_method(self, obj: Any) -> tuple:\n         if isinstance(obj, Module):\n", "issue": "NVTX annotation of multiple use of a transform\n**Is your feature request related to a problem? Please describe.**\r\nNVTX Range decorator currently is able to to patch each transform once. Multiple use of the same transforms with `Range` will result in duplicate annotations.\r\n\n", "before_files": [{"content": "# Copyright 2020 - 2021 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nDecorators and context managers for NVIDIA Tools Extension to profile MONAI components\n\"\"\"\n\nfrom collections import defaultdict\nfrom functools import wraps\nfrom typing import Any, Optional, Tuple, Union\n\nfrom torch.autograd import Function\nfrom torch.nn import Module\nfrom torch.optim import Optimizer\nfrom torch.utils.data import Dataset\n\nfrom monai.utils import ensure_tuple, optional_import\n\n_nvtx, _ = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\n__all__ = [\"Range\"]\n\n\nclass Range:\n    \"\"\"\n    A decorator and context manager for NVIDIA Tools Extension (NVTX) Range for profiling.\n    When used as a decorator it encloses a specific method of the object with an NVTX Range.\n    When used as a context manager, it encloses the runtime context (created by with statement) with an NVTX Range.\n\n    Args:\n        name: the name to be associated to the range\n        methods: (only when used as decorator) the name of a method (or a list of the name of the methods)\n            to be wrapped by NVTX range.\n            If None (default), the method(s) will be inferred based on the object's type for various MONAI components,\n            such as Networks, Losses, Functions, Transforms, and Datasets.\n            Otherwise, it look up predefined methods: \"forward\", \"__call__\", \"__next__\", \"__getitem__\"\n        append_method_name: if append the name of the methods to be decorated to the range's name\n            If None (default), it appends the method's name only if we are annotating more than one method.\n\n    \"\"\"\n\n    name_counter: dict = defaultdict(int)\n\n    def __init__(\n        self,\n        name: Optional[str] = None,\n        methods: Optional[Union[str, Tuple[str, ...]]] = None,\n        append_method_name: Optional[bool] = None,\n    ) -> None:\n        self.name = name\n        self.methods = methods\n        self.append_method_name = append_method_name\n\n    def __call__(self, obj: Any):\n        # Define the name to be associated to the range if not provided\n        if self.name is None:\n            name = type(obj).__name__\n            self.name_counter[name] += 1\n            self.name = f\"{name}_{self.name_counter[name]}\"\n\n        # Define the methods to be wrapped if not provided\n        if self.methods is None:\n            self.methods = self._get_method(obj)\n        else:\n            self.methods = ensure_tuple(self.methods)\n\n        # Check if to append method's name to the range's name\n        if self.append_method_name is None:\n            if len(self.methods) > 1:\n                self.append_method_name = True\n            else:\n                self.append_method_name = False\n\n        # Decorate the methods\n        for method in self.methods:\n            self._decorate_method(obj, method, self.append_method_name)\n\n        return obj\n\n    def _decorate_method(self, obj, method, append_method_name):\n        # Append the method's name to the range's name\n        if append_method_name:\n            name = f\"{self.name}.{method}\"\n        else:\n            name = self.name\n\n        # Get the class for special functions\n        if method.startswith(\"_\"):\n            owner = type(obj)\n        else:\n            owner = obj\n\n        # Get the method to be wrapped\n        _temp_func = getattr(owner, method)\n\n        # Wrap the method with NVTX range (range push/pop)\n        @wraps(_temp_func)\n        def range_wrapper(*args, **kwargs):\n            _nvtx.rangePushA(name)\n            output = _temp_func(*args, **kwargs)\n            _nvtx.rangePop()\n            return output\n\n        # Replace the method with the wrapped version\n        setattr(owner, method, range_wrapper)\n\n    def _get_method(self, obj: Any) -> tuple:\n        if isinstance(obj, Module):\n            method_list = [\"forward\"]\n        elif isinstance(obj, Optimizer):\n            method_list = [\"step\"]\n        elif isinstance(obj, Function):\n            method_list = [\"forward\", \"backward\"]\n        elif isinstance(obj, Dataset):\n            method_list = [\"__getitem__\"]\n        else:\n            default_methods = [\"forward\", \"__call__\", \"__next__\", \"__getitem__\"]\n            method_list = []\n            for method in default_methods:\n                if hasattr(obj, method):\n                    method_list.append(method)\n            if len(method_list) < 1:\n                raise ValueError(\n                    f\"The method to be wrapped for this object [{type(obj)}] is not recognized.\"\n                    \"The name of the method should be provied or the object should have one of these methods:\"\n                    f\"{default_methods}\"\n                )\n        return ensure_tuple(method_list)\n\n    def __enter__(self):\n        if self.name is None:\n            # Number the range with class variable counter to avoid duplicate names.\n            self.name_counter[\"context\"] += 1\n            self.name = f\"context_{self.name_counter['context']}\"\n\n        _nvtx.rangePushA(self.name)\n\n    def __exit__(self, type, value, traceback):\n        _nvtx.rangePop()\n", "path": "monai/utils/nvtx.py"}], "after_files": [{"content": "# Copyright 2020 - 2021 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nDecorators and context managers for NVIDIA Tools Extension to profile MONAI components\n\"\"\"\n\nfrom collections import defaultdict\nfrom functools import wraps\nfrom typing import Any, Optional, Tuple, Union\n\nfrom torch.autograd import Function\nfrom torch.nn import Module\nfrom torch.optim import Optimizer\nfrom torch.utils.data import Dataset\n\nfrom monai.utils import ensure_tuple, optional_import\n\n_nvtx, _ = optional_import(\"torch._C._nvtx\", descriptor=\"NVTX is not installed. Are you sure you have a CUDA build?\")\n\n__all__ = [\"Range\"]\n\n\nclass Range:\n    \"\"\"\n    A decorator and context manager for NVIDIA Tools Extension (NVTX) Range for profiling.\n    When used as a decorator it encloses a specific method of the object with an NVTX Range.\n    When used as a context manager, it encloses the runtime context (created by with statement) with an NVTX Range.\n\n    Args:\n        name: the name to be associated to the range\n        methods: (only when used as decorator) the name of a method (or a list of the name of the methods)\n            to be wrapped by NVTX range.\n            If None (default), the method(s) will be inferred based on the object's type for various MONAI components,\n            such as Networks, Losses, Functions, Transforms, and Datasets.\n            Otherwise, it look up predefined methods: \"forward\", \"__call__\", \"__next__\", \"__getitem__\"\n        append_method_name: if append the name of the methods to be decorated to the range's name\n            If None (default), it appends the method's name only if we are annotating more than one method.\n\n    \"\"\"\n\n    name_counter: dict = defaultdict(int)\n\n    def __init__(\n        self,\n        name: Optional[str] = None,\n        methods: Optional[Union[str, Tuple[str, ...]]] = None,\n        append_method_name: Optional[bool] = None,\n    ) -> None:\n        self.name = name\n        self.methods = methods\n        self.append_method_name = append_method_name\n\n    def __call__(self, obj: Any):\n        # Define the name to be associated to the range if not provided\n        if self.name is None:\n            name = type(obj).__name__\n            self.name_counter[name] += 1\n            self.name = f\"{name}_{self.name_counter[name]}\"\n\n        # Define the methods to be wrapped if not provided\n        if self.methods is None:\n            self.methods = self._get_method(obj)\n        else:\n            self.methods = ensure_tuple(self.methods)\n\n        # Check if to append method's name to the range's name\n        if self.append_method_name is None:\n            if len(self.methods) > 1:\n                self.append_method_name = True\n            else:\n                self.append_method_name = False\n\n        # Decorate the methods\n        for method in self.methods:\n            self._decorate_method(obj, method, self.append_method_name)\n\n        return obj\n\n    def _decorate_method(self, obj, method, append_method_name):\n        # Append the method's name to the range's name\n        if append_method_name:\n            name = f\"{self.name}.{method}\"\n        else:\n            name = self.name\n\n        # Get the class for special functions\n        if method.startswith(\"__\"):\n            owner = type(obj)\n        else:\n            owner = obj\n\n        # Get the method to be wrapped\n        _temp_func = getattr(owner, method)\n\n        # Wrap the method with NVTX range (range push/pop)\n        @wraps(_temp_func)\n        def range_wrapper(*args, **kwargs):\n            _nvtx.rangePushA(name)\n            output = _temp_func(*args, **kwargs)\n            _nvtx.rangePop()\n            return output\n\n        # Replace the method with the wrapped version\n        if method.startswith(\"__\"):\n            # If it is a special method, it requires special attention\n            class NVTXRangeDecoratedClass(owner):\n                ...\n\n            setattr(NVTXRangeDecoratedClass, method, range_wrapper)\n            obj.__class__ = NVTXRangeDecoratedClass\n\n        else:\n            setattr(owner, method, range_wrapper)\n\n    def _get_method(self, obj: Any) -> tuple:\n        if isinstance(obj, Module):\n            method_list = [\"forward\"]\n        elif isinstance(obj, Optimizer):\n            method_list = [\"step\"]\n        elif isinstance(obj, Function):\n            method_list = [\"forward\", \"backward\"]\n        elif isinstance(obj, Dataset):\n            method_list = [\"__getitem__\"]\n        else:\n            default_methods = [\"forward\", \"__call__\", \"__next__\", \"__getitem__\"]\n            method_list = []\n            for method in default_methods:\n                if hasattr(obj, method):\n                    method_list.append(method)\n            if len(method_list) < 1:\n                raise ValueError(\n                    f\"The method to be wrapped for this object [{type(obj)}] is not recognized.\"\n                    \"The name of the method should be provied or the object should have one of these methods:\"\n                    f\"{default_methods}\"\n                )\n        return ensure_tuple(method_list)\n\n    def __enter__(self):\n        if self.name is None:\n            # Number the range with class variable counter to avoid duplicate names.\n            self.name_counter[\"context\"] += 1\n            self.name = f\"context_{self.name_counter['context']}\"\n\n        _nvtx.rangePushA(self.name)\n\n    def __exit__(self, type, value, traceback):\n        _nvtx.rangePop()\n", "path": "monai/utils/nvtx.py"}]}