prompt: 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()
  
  ```
  
golden_diff: 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)
  
verification_info: {"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"}]}