prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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.
  
  </issue>
  <code>
  [start of examples/kubernetes/simple/sklearn_distributed.py]
  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 of examples/kubernetes/simple/sklearn_distributed.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
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"}]}