We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Update Kubernetes example of "simple" to avoid potential errors
https://github.com/optuna/optuna/blob/a5a55a6354b3c60dd7e3a08adcbc3818e90599f0/examples/kubernetes/simple/sklearn_distributed.py#L45-L54

Like https://github.com/optuna/optuna/pull/1536, we should create a study before running the script.

--- 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/kubernetes/simple/sklearn_distributed.py`
Content:
```
1 """
2 Optuna distributed optimization example that optimizes
3 an sklearn classifier for the Iris dataset on Kubernetes.
4 
5 This example's code is mostly the same as the sklearn_simple.py example, except for two things:
6 
7 1 - It gives a name to the study and sets load_if_exists to True
8 in order to avoid errors when the code is run from multiple workers.
9 
10 2 - It sets the storage address to the postgres pod deployed with the workers.
11 
12 """
13 import os
14 
15 import sklearn.datasets
16 import sklearn.ensemble
17 import sklearn.model_selection
18 import sklearn.svm
19 
20 import optuna
21 
22 
23 # FYI: Objective functions can take additional arguments
24 # (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
25 def objective(trial):
26     iris = sklearn.datasets.load_iris()
27     x, y = iris.data, iris.target
28 
29     classifier_name = trial.suggest_categorical("classifier", ["SVC", "RandomForest"])
30     if classifier_name == "SVC":
31         svc_c = trial.suggest_float("svc_c", 1e-10, 1e10, log=True)
32         classifier_obj = sklearn.svm.SVC(C=svc_c, gamma="auto")
33     else:
34         rf_max_depth = trial.suggest_int("rf_max_depth", 2, 32, log=True)
35         classifier_obj = sklearn.ensemble.RandomForestClassifier(
36             max_depth=rf_max_depth, n_estimators=10
37         )
38 
39     score = sklearn.model_selection.cross_val_score(classifier_obj, x, y, n_jobs=-1, cv=3)
40     accuracy = score.mean()
41     return accuracy
42 
43 
44 if __name__ == "__main__":
45     study = optuna.create_study(
46         direction="maximize",
47         study_name="kubernetes",
48         storage="postgresql://{}:{}@postgres:5432/{}".format(
49             os.environ["POSTGRES_USER"],
50             os.environ["POSTGRES_PASSWORD"],
51             os.environ["POSTGRES_DB"],
52         ),
53         load_if_exists=True,
54     )
55     study.optimize(objective, n_trials=20)
56     print(study.best_trial)
57
```
--- 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/kubernetes/simple/sklearn_distributed.py b/examples/kubernetes/simple/sklearn_distributed.py
--- a/examples/kubernetes/simple/sklearn_distributed.py
+++ b/examples/kubernetes/simple/sklearn_distributed.py
@@ -42,15 +42,13 @@
 
 
 if __name__ == "__main__":
-    study = optuna.create_study(
-        direction="maximize",
+    study = optuna.load_study(
         study_name="kubernetes",
         storage="postgresql://{}:{}@postgres:5432/{}".format(
             os.environ["POSTGRES_USER"],
             os.environ["POSTGRES_PASSWORD"],
             os.environ["POSTGRES_DB"],
         ),
-        load_if_exists=True,
     )
     study.optimize(objective, n_trials=20)
     print(study.best_trial)

{"golden_diff": "diff --git a/examples/kubernetes/simple/sklearn_distributed.py b/examples/kubernetes/simple/sklearn_distributed.py\n--- a/examples/kubernetes/simple/sklearn_distributed.py\n+++ b/examples/kubernetes/simple/sklearn_distributed.py\n@@ -42,15 +42,13 @@\n \n \n if __name__ == \"__main__\":\n-    study = optuna.create_study(\n-        direction=\"maximize\",\n+    study = optuna.load_study(\n         study_name=\"kubernetes\",\n         storage=\"postgresql://{}:{}@postgres:5432/{}\".format(\n             os.environ[\"POSTGRES_USER\"],\n             os.environ[\"POSTGRES_PASSWORD\"],\n             os.environ[\"POSTGRES_DB\"],\n         ),\n-        load_if_exists=True,\n     )\n     study.optimize(objective, n_trials=20)\n     print(study.best_trial)\n", "issue": "Update Kubernetes example of \"simple\" to avoid potential errors\nhttps://github.com/optuna/optuna/blob/a5a55a6354b3c60dd7e3a08adcbc3818e90599f0/examples/kubernetes/simple/sklearn_distributed.py#L45-L54\r\n\r\nLike https://github.com/optuna/optuna/pull/1536, we should create a study before running the script.\n", "before_files": [{"content": "\"\"\"\nOptuna distributed optimization example that optimizes\nan sklearn classifier for the Iris dataset on Kubernetes.\n\nThis example's code is mostly the same as the sklearn_simple.py example, except for two things:\n\n1 - It gives a name to the study and sets load_if_exists to True\nin order to avoid errors when the code is run from multiple workers.\n\n2 - It sets the storage address to the postgres pod deployed with the workers.\n\n\"\"\"\nimport os\n\nimport sklearn.datasets\nimport sklearn.ensemble\nimport sklearn.model_selection\nimport sklearn.svm\n\nimport optuna\n\n\n# FYI: Objective functions can take additional arguments\n# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).\ndef objective(trial):\n    iris = sklearn.datasets.load_iris()\n    x, y = iris.data, iris.target\n\n    classifier_name = trial.suggest_categorical(\"classifier\", [\"SVC\", \"RandomForest\"])\n    if classifier_name == \"SVC\":\n        svc_c = trial.suggest_float(\"svc_c\", 1e-10, 1e10, log=True)\n        classifier_obj = sklearn.svm.SVC(C=svc_c, gamma=\"auto\")\n    else:\n        rf_max_depth = trial.suggest_int(\"rf_max_depth\", 2, 32, log=True)\n        classifier_obj = sklearn.ensemble.RandomForestClassifier(\n            max_depth=rf_max_depth, n_estimators=10\n        )\n\n    score = sklearn.model_selection.cross_val_score(classifier_obj, x, y, n_jobs=-1, cv=3)\n    accuracy = score.mean()\n    return accuracy\n\n\nif __name__ == \"__main__\":\n    study = optuna.create_study(\n        direction=\"maximize\",\n        study_name=\"kubernetes\",\n        storage=\"postgresql://{}:{}@postgres:5432/{}\".format(\n            os.environ[\"POSTGRES_USER\"],\n            os.environ[\"POSTGRES_PASSWORD\"],\n            os.environ[\"POSTGRES_DB\"],\n        ),\n        load_if_exists=True,\n    )\n    study.optimize(objective, n_trials=20)\n    print(study.best_trial)\n", "path": "examples/kubernetes/simple/sklearn_distributed.py"}], "after_files": [{"content": "\"\"\"\nOptuna distributed optimization example that optimizes\nan sklearn classifier for the Iris dataset on Kubernetes.\n\nThis example's code is mostly the same as the sklearn_simple.py example, except for two things:\n\n1 - It gives a name to the study and sets load_if_exists to True\nin order to avoid errors when the code is run from multiple workers.\n\n2 - It sets the storage address to the postgres pod deployed with the workers.\n\n\"\"\"\nimport os\n\nimport sklearn.datasets\nimport sklearn.ensemble\nimport sklearn.model_selection\nimport sklearn.svm\n\nimport optuna\n\n\n# FYI: Objective functions can take additional arguments\n# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).\ndef objective(trial):\n    iris = sklearn.datasets.load_iris()\n    x, y = iris.data, iris.target\n\n    classifier_name = trial.suggest_categorical(\"classifier\", [\"SVC\", \"RandomForest\"])\n    if classifier_name == \"SVC\":\n        svc_c = trial.suggest_float(\"svc_c\", 1e-10, 1e10, log=True)\n        classifier_obj = sklearn.svm.SVC(C=svc_c, gamma=\"auto\")\n    else:\n        rf_max_depth = trial.suggest_int(\"rf_max_depth\", 2, 32, log=True)\n        classifier_obj = sklearn.ensemble.RandomForestClassifier(\n            max_depth=rf_max_depth, n_estimators=10\n        )\n\n    score = sklearn.model_selection.cross_val_score(classifier_obj, x, y, n_jobs=-1, cv=3)\n    accuracy = score.mean()\n    return accuracy\n\n\nif __name__ == \"__main__\":\n    study = optuna.load_study(\n        study_name=\"kubernetes\",\n        storage=\"postgresql://{}:{}@postgres:5432/{}\".format(\n            os.environ[\"POSTGRES_USER\"],\n            os.environ[\"POSTGRES_PASSWORD\"],\n            os.environ[\"POSTGRES_DB\"],\n        ),\n    )\n    study.optimize(objective, n_trials=20)\n    print(study.best_trial)\n", "path": "examples/kubernetes/simple/sklearn_distributed.py"}]}