We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Bug in prostate_2D example
https://github.com/NVIDIA/NVFlare/blob/8f8f029eeecf58a85d9633357ce1ed4f8f39f655/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py#L171

`self.transform_valid` is not defined if `cache_rate=0`.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py`
Content:
```
1 # Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 import json
16 import os
17 
18 import torch
19 import torch.optim as optim
20 from learners.supervised_learner import SupervisedLearner
21 from monai.data import CacheDataset, DataLoader, Dataset, load_decathlon_datalist
22 from monai.inferers import SimpleInferer
23 from monai.losses import DiceLoss
24 from monai.metrics import DiceMetric
25 from monai.networks.nets.unet import UNet
26 from monai.transforms import (
27     Activations,
28     AsDiscrete,
29     AsDiscreted,
30     Compose,
31     EnsureChannelFirstd,
32     EnsureType,
33     EnsureTyped,
34     LoadImaged,
35     Resized,
36     ScaleIntensityRanged,
37 )
38 from utils.custom_client_datalist_json_path import custom_client_datalist_json_path
39 
40 from nvflare.apis.fl_context import FLContext
41 from nvflare.app_common.app_constant import AppConstants
42 from nvflare.app_common.pt.pt_fedproxloss import PTFedProxLoss
43 
44 
45 class SupervisedMonaiProstateLearner(SupervisedLearner):
46     def __init__(
47         self,
48         train_config_filename,
49         aggregation_epochs: int = 1,
50         train_task_name: str = AppConstants.TASK_TRAIN,
51     ):
52         """MONAI Learner for prostate segmentation task.
53         It inherits from SupervisedLearner.
54 
55         Args:
56             train_config_filename: path for config file, this is an addition term for config loading
57             aggregation_epochs: the number of training epochs for a round.
58             train_task_name: name of the task to train the model.
59 
60         Returns:
61             a Shareable with the updated local model after running `execute()`
62         """
63         super().__init__(
64             aggregation_epochs=aggregation_epochs,
65             train_task_name=train_task_name,
66         )
67         self.train_config_filename = train_config_filename
68         self.config_info = None
69 
70     def train_config(self, fl_ctx: FLContext):
71         """MONAI traning configuration
72         Here, we use a json to specify the needed parameters
73         """
74 
75         # Load training configurations json
76         engine = fl_ctx.get_engine()
77         ws = engine.get_workspace()
78         app_config_dir = ws.get_app_config_dir(fl_ctx.get_job_id())
79         train_config_file_path = os.path.join(app_config_dir, self.train_config_filename)
80         if not os.path.isfile(train_config_file_path):
81             self.log_error(
82                 fl_ctx,
83                 f"Training configuration file does not exist at {train_config_file_path}",
84             )
85         with open(train_config_file_path) as file:
86             self.config_info = json.load(file)
87 
88         # Get the config_info
89         self.lr = self.config_info["learning_rate"]
90         self.fedproxloss_mu = self.config_info["fedproxloss_mu"]
91         cache_rate = self.config_info["cache_dataset"]
92         dataset_base_dir = self.config_info["dataset_base_dir"]
93         datalist_json_path = self.config_info["datalist_json_path"]
94 
95         # Get datalist json
96         datalist_json_path = custom_client_datalist_json_path(datalist_json_path, self.client_id)
97 
98         # Set datalist
99         train_list = load_decathlon_datalist(
100             data_list_file_path=datalist_json_path,
101             is_segmentation=True,
102             data_list_key="training",
103             base_dir=dataset_base_dir,
104         )
105         valid_list = load_decathlon_datalist(
106             data_list_file_path=datalist_json_path,
107             is_segmentation=True,
108             data_list_key="validation",
109             base_dir=dataset_base_dir,
110         )
111         self.log_info(
112             fl_ctx,
113             f"Training Size: {len(train_list)}, Validation Size: {len(valid_list)}",
114         )
115 
116         # Set the training-related context
117         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
118         self.model = UNet(
119             spatial_dims=2,
120             in_channels=1,
121             out_channels=1,
122             channels=(16, 32, 64, 128, 256),
123             strides=(2, 2, 2, 2),
124             num_res_units=2,
125         ).to(self.device)
126         self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
127         self.criterion = DiceLoss(sigmoid=True)
128 
129         if self.fedproxloss_mu > 0:
130             self.log_info(fl_ctx, f"using FedProx loss with mu {self.fedproxloss_mu}")
131             self.criterion_prox = PTFedProxLoss(mu=self.fedproxloss_mu)
132 
133         self.transform = Compose(
134             [
135                 LoadImaged(keys=["image", "label"]),
136                 EnsureChannelFirstd(keys=["image", "label"]),
137                 ScaleIntensityRanged(keys=["image", "label"], a_min=0, a_max=255, b_min=0.0, b_max=1.0),
138                 Resized(
139                     keys=["image", "label"],
140                     spatial_size=(256, 256),
141                     mode=("bilinear"),
142                     align_corners=True,
143                 ),
144                 AsDiscreted(keys=["label"], threshold=0.5),
145                 EnsureTyped(keys=["image", "label"]),
146             ]
147         )
148         self.transform_post = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
149 
150         # Set dataset
151         if cache_rate > 0.0:
152             self.train_dataset = CacheDataset(
153                 data=train_list,
154                 transform=self.transform,
155                 cache_rate=cache_rate,
156                 num_workers=4,
157             )
158             self.valid_dataset = CacheDataset(
159                 data=valid_list,
160                 transform=self.transform,
161                 cache_rate=cache_rate,
162                 num_workers=4,
163             )
164         else:
165             self.train_dataset = Dataset(
166                 data=train_list,
167                 transform=self.transform,
168             )
169             self.valid_dataset = Dataset(
170                 data=valid_list,
171                 transform=self.transform_valid,
172             )
173 
174         self.train_loader = DataLoader(
175             self.train_dataset,
176             batch_size=1,
177             shuffle=True,
178             num_workers=2,
179         )
180         self.valid_loader = DataLoader(
181             self.valid_dataset,
182             batch_size=1,
183             shuffle=False,
184             num_workers=2,
185         )
186 
187         # Set inferer and evaluation metric
188         self.inferer = SimpleInferer()
189         self.valid_metric = DiceMetric(include_background=False, reduction="mean", get_not_nans=False)
190
```
--- 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/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py b/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py
--- a/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py
+++ b/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py
@@ -168,7 +168,7 @@
             )
             self.valid_dataset = Dataset(
                 data=valid_list,
-                transform=self.transform_valid,
+                transform=self.transform,
             )
 
         self.train_loader = DataLoader(

{"golden_diff": "diff --git a/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py b/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py\n--- a/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py\n+++ b/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py\n@@ -168,7 +168,7 @@\n             )\n             self.valid_dataset = Dataset(\n                 data=valid_list,\n-                transform=self.transform_valid,\n+                transform=self.transform,\n             )\n \n         self.train_loader = DataLoader(\n", "issue": "Bug in prostate_2D example\nhttps://github.com/NVIDIA/NVFlare/blob/8f8f029eeecf58a85d9633357ce1ed4f8f39f655/examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py#L171\r\n\r\n`self.transform_valid` is not defined if `cache_rate=0`.\n", "before_files": [{"content": "# Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.\n#\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#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\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\nimport json\nimport os\n\nimport torch\nimport torch.optim as optim\nfrom learners.supervised_learner import SupervisedLearner\nfrom monai.data import CacheDataset, DataLoader, Dataset, load_decathlon_datalist\nfrom monai.inferers import SimpleInferer\nfrom monai.losses import DiceLoss\nfrom monai.metrics import DiceMetric\nfrom monai.networks.nets.unet import UNet\nfrom monai.transforms import (\n    Activations,\n    AsDiscrete,\n    AsDiscreted,\n    Compose,\n    EnsureChannelFirstd,\n    EnsureType,\n    EnsureTyped,\n    LoadImaged,\n    Resized,\n    ScaleIntensityRanged,\n)\nfrom utils.custom_client_datalist_json_path import custom_client_datalist_json_path\n\nfrom nvflare.apis.fl_context import FLContext\nfrom nvflare.app_common.app_constant import AppConstants\nfrom nvflare.app_common.pt.pt_fedproxloss import PTFedProxLoss\n\n\nclass SupervisedMonaiProstateLearner(SupervisedLearner):\n    def __init__(\n        self,\n        train_config_filename,\n        aggregation_epochs: int = 1,\n        train_task_name: str = AppConstants.TASK_TRAIN,\n    ):\n        \"\"\"MONAI Learner for prostate segmentation task.\n        It inherits from SupervisedLearner.\n\n        Args:\n            train_config_filename: path for config file, this is an addition term for config loading\n            aggregation_epochs: the number of training epochs for a round.\n            train_task_name: name of the task to train the model.\n\n        Returns:\n            a Shareable with the updated local model after running `execute()`\n        \"\"\"\n        super().__init__(\n            aggregation_epochs=aggregation_epochs,\n            train_task_name=train_task_name,\n        )\n        self.train_config_filename = train_config_filename\n        self.config_info = None\n\n    def train_config(self, fl_ctx: FLContext):\n        \"\"\"MONAI traning configuration\n        Here, we use a json to specify the needed parameters\n        \"\"\"\n\n        # Load training configurations json\n        engine = fl_ctx.get_engine()\n        ws = engine.get_workspace()\n        app_config_dir = ws.get_app_config_dir(fl_ctx.get_job_id())\n        train_config_file_path = os.path.join(app_config_dir, self.train_config_filename)\n        if not os.path.isfile(train_config_file_path):\n            self.log_error(\n                fl_ctx,\n                f\"Training configuration file does not exist at {train_config_file_path}\",\n            )\n        with open(train_config_file_path) as file:\n            self.config_info = json.load(file)\n\n        # Get the config_info\n        self.lr = self.config_info[\"learning_rate\"]\n        self.fedproxloss_mu = self.config_info[\"fedproxloss_mu\"]\n        cache_rate = self.config_info[\"cache_dataset\"]\n        dataset_base_dir = self.config_info[\"dataset_base_dir\"]\n        datalist_json_path = self.config_info[\"datalist_json_path\"]\n\n        # Get datalist json\n        datalist_json_path = custom_client_datalist_json_path(datalist_json_path, self.client_id)\n\n        # Set datalist\n        train_list = load_decathlon_datalist(\n            data_list_file_path=datalist_json_path,\n            is_segmentation=True,\n            data_list_key=\"training\",\n            base_dir=dataset_base_dir,\n        )\n        valid_list = load_decathlon_datalist(\n            data_list_file_path=datalist_json_path,\n            is_segmentation=True,\n            data_list_key=\"validation\",\n            base_dir=dataset_base_dir,\n        )\n        self.log_info(\n            fl_ctx,\n            f\"Training Size: {len(train_list)}, Validation Size: {len(valid_list)}\",\n        )\n\n        # Set the training-related context\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n        self.model = UNet(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=1,\n            channels=(16, 32, 64, 128, 256),\n            strides=(2, 2, 2, 2),\n            num_res_units=2,\n        ).to(self.device)\n        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)\n        self.criterion = DiceLoss(sigmoid=True)\n\n        if self.fedproxloss_mu > 0:\n            self.log_info(fl_ctx, f\"using FedProx loss with mu {self.fedproxloss_mu}\")\n            self.criterion_prox = PTFedProxLoss(mu=self.fedproxloss_mu)\n\n        self.transform = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n                ScaleIntensityRanged(keys=[\"image\", \"label\"], a_min=0, a_max=255, b_min=0.0, b_max=1.0),\n                Resized(\n                    keys=[\"image\", \"label\"],\n                    spatial_size=(256, 256),\n                    mode=(\"bilinear\"),\n                    align_corners=True,\n                ),\n                AsDiscreted(keys=[\"label\"], threshold=0.5),\n                EnsureTyped(keys=[\"image\", \"label\"]),\n            ]\n        )\n        self.transform_post = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n\n        # Set dataset\n        if cache_rate > 0.0:\n            self.train_dataset = CacheDataset(\n                data=train_list,\n                transform=self.transform,\n                cache_rate=cache_rate,\n                num_workers=4,\n            )\n            self.valid_dataset = CacheDataset(\n                data=valid_list,\n                transform=self.transform,\n                cache_rate=cache_rate,\n                num_workers=4,\n            )\n        else:\n            self.train_dataset = Dataset(\n                data=train_list,\n                transform=self.transform,\n            )\n            self.valid_dataset = Dataset(\n                data=valid_list,\n                transform=self.transform_valid,\n            )\n\n        self.train_loader = DataLoader(\n            self.train_dataset,\n            batch_size=1,\n            shuffle=True,\n            num_workers=2,\n        )\n        self.valid_loader = DataLoader(\n            self.valid_dataset,\n            batch_size=1,\n            shuffle=False,\n            num_workers=2,\n        )\n\n        # Set inferer and evaluation metric\n        self.inferer = SimpleInferer()\n        self.valid_metric = DiceMetric(include_background=False, reduction=\"mean\", get_not_nans=False)\n", "path": "examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py"}], "after_files": [{"content": "# Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.\n#\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#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\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\nimport json\nimport os\n\nimport torch\nimport torch.optim as optim\nfrom learners.supervised_learner import SupervisedLearner\nfrom monai.data import CacheDataset, DataLoader, Dataset, load_decathlon_datalist\nfrom monai.inferers import SimpleInferer\nfrom monai.losses import DiceLoss\nfrom monai.metrics import DiceMetric\nfrom monai.networks.nets.unet import UNet\nfrom monai.transforms import (\n    Activations,\n    AsDiscrete,\n    AsDiscreted,\n    Compose,\n    EnsureChannelFirstd,\n    EnsureType,\n    EnsureTyped,\n    LoadImaged,\n    Resized,\n    ScaleIntensityRanged,\n)\nfrom utils.custom_client_datalist_json_path import custom_client_datalist_json_path\n\nfrom nvflare.apis.fl_context import FLContext\nfrom nvflare.app_common.app_constant import AppConstants\nfrom nvflare.app_common.pt.pt_fedproxloss import PTFedProxLoss\n\n\nclass SupervisedMonaiProstateLearner(SupervisedLearner):\n    def __init__(\n        self,\n        train_config_filename,\n        aggregation_epochs: int = 1,\n        train_task_name: str = AppConstants.TASK_TRAIN,\n    ):\n        \"\"\"MONAI Learner for prostate segmentation task.\n        It inherits from SupervisedLearner.\n\n        Args:\n            train_config_filename: path for config file, this is an addition term for config loading\n            aggregation_epochs: the number of training epochs for a round.\n            train_task_name: name of the task to train the model.\n\n        Returns:\n            a Shareable with the updated local model after running `execute()`\n        \"\"\"\n        super().__init__(\n            aggregation_epochs=aggregation_epochs,\n            train_task_name=train_task_name,\n        )\n        self.train_config_filename = train_config_filename\n        self.config_info = None\n\n    def train_config(self, fl_ctx: FLContext):\n        \"\"\"MONAI traning configuration\n        Here, we use a json to specify the needed parameters\n        \"\"\"\n\n        # Load training configurations json\n        engine = fl_ctx.get_engine()\n        ws = engine.get_workspace()\n        app_config_dir = ws.get_app_config_dir(fl_ctx.get_job_id())\n        train_config_file_path = os.path.join(app_config_dir, self.train_config_filename)\n        if not os.path.isfile(train_config_file_path):\n            self.log_error(\n                fl_ctx,\n                f\"Training configuration file does not exist at {train_config_file_path}\",\n            )\n        with open(train_config_file_path) as file:\n            self.config_info = json.load(file)\n\n        # Get the config_info\n        self.lr = self.config_info[\"learning_rate\"]\n        self.fedproxloss_mu = self.config_info[\"fedproxloss_mu\"]\n        cache_rate = self.config_info[\"cache_dataset\"]\n        dataset_base_dir = self.config_info[\"dataset_base_dir\"]\n        datalist_json_path = self.config_info[\"datalist_json_path\"]\n\n        # Get datalist json\n        datalist_json_path = custom_client_datalist_json_path(datalist_json_path, self.client_id)\n\n        # Set datalist\n        train_list = load_decathlon_datalist(\n            data_list_file_path=datalist_json_path,\n            is_segmentation=True,\n            data_list_key=\"training\",\n            base_dir=dataset_base_dir,\n        )\n        valid_list = load_decathlon_datalist(\n            data_list_file_path=datalist_json_path,\n            is_segmentation=True,\n            data_list_key=\"validation\",\n            base_dir=dataset_base_dir,\n        )\n        self.log_info(\n            fl_ctx,\n            f\"Training Size: {len(train_list)}, Validation Size: {len(valid_list)}\",\n        )\n\n        # Set the training-related context\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n        self.model = UNet(\n            spatial_dims=2,\n            in_channels=1,\n            out_channels=1,\n            channels=(16, 32, 64, 128, 256),\n            strides=(2, 2, 2, 2),\n            num_res_units=2,\n        ).to(self.device)\n        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)\n        self.criterion = DiceLoss(sigmoid=True)\n\n        if self.fedproxloss_mu > 0:\n            self.log_info(fl_ctx, f\"using FedProx loss with mu {self.fedproxloss_mu}\")\n            self.criterion_prox = PTFedProxLoss(mu=self.fedproxloss_mu)\n\n        self.transform = Compose(\n            [\n                LoadImaged(keys=[\"image\", \"label\"]),\n                EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n                ScaleIntensityRanged(keys=[\"image\", \"label\"], a_min=0, a_max=255, b_min=0.0, b_max=1.0),\n                Resized(\n                    keys=[\"image\", \"label\"],\n                    spatial_size=(256, 256),\n                    mode=(\"bilinear\"),\n                    align_corners=True,\n                ),\n                AsDiscreted(keys=[\"label\"], threshold=0.5),\n                EnsureTyped(keys=[\"image\", \"label\"]),\n            ]\n        )\n        self.transform_post = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n\n        # Set dataset\n        if cache_rate > 0.0:\n            self.train_dataset = CacheDataset(\n                data=train_list,\n                transform=self.transform,\n                cache_rate=cache_rate,\n                num_workers=4,\n            )\n            self.valid_dataset = CacheDataset(\n                data=valid_list,\n                transform=self.transform,\n                cache_rate=cache_rate,\n                num_workers=4,\n            )\n        else:\n            self.train_dataset = Dataset(\n                data=train_list,\n                transform=self.transform,\n            )\n            self.valid_dataset = Dataset(\n                data=valid_list,\n                transform=self.transform,\n            )\n\n        self.train_loader = DataLoader(\n            self.train_dataset,\n            batch_size=1,\n            shuffle=True,\n            num_workers=2,\n        )\n        self.valid_loader = DataLoader(\n            self.valid_dataset,\n            batch_size=1,\n            shuffle=False,\n            num_workers=2,\n        )\n\n        # Set inferer and evaluation metric\n        self.inferer = SimpleInferer()\n        self.valid_metric = DiceMetric(include_background=False, reduction=\"mean\", get_not_nans=False)\n", "path": "examples/advanced/prostate/prostate_2D/custom/learners/supervised_monai_prostate_learner.py"}]}