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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Logging results and uploading models to Comet ML
In this example, we train a simple XGBoost model and log the training
results to Comet ML. We also save the resulting model checkpoints
as artifacts.
Let's start with installing our dependencies
Step1:  Then we need some imports
Step3:  We define a simple function that returns our training dataset as a Ray Dataset
Step5:  Now we define a simple training function. All the magic happens within the CometLoggerCallback
Step6:  Let's kick off a run
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Python Code:
!pip install -qU "ray[tune]" sklearn xgboost_ray comet_ml
Explanation: Logging results and uploading models to Comet ML
In this example, we train a simple XGBoost model and log the training
results to Comet ML. We also save the resulting model checkpoints
as artifacts.
Let's start with installing our dependencies:
End of explanation
import ray
from ray.air import RunConfig
from ray.air.result import Result
from ray.train.xgboost import XGBoostTrainer
from ray.tune.integration.comet import CometLoggerCallback
from sklearn.datasets import load_breast_cancer
Explanation: Then we need some imports:
End of explanation
def get_train_dataset() -> ray.data.Dataset:
    Return the "Breast cancer" dataset as a Ray dataset.
    data_raw = load_breast_cancer(as_frame=True)
    df = data_raw["data"]
    df["target"] = data_raw["target"]
    return ray.data.from_pandas(df)
Explanation: We define a simple function that returns our training dataset as a Ray Dataset:
End of explanation
def train_model(train_dataset: ray.data.Dataset, comet_project: str) -> Result:
    Train a simple XGBoost model and return the result.
    trainer = XGBoostTrainer(
        scaling_config={"num_workers": 2},
        params={"tree_method": "auto"},
        label_column="target",
        datasets={"train": train_dataset},
        num_boost_round=10,
        run_config=RunConfig(
            callbacks=[
                # This is the part needed to enable logging to Comet ML.
                # It assumes Comet ML can find a valid API (e.g. by setting
                # the ``COMET_API_KEY`` environment variable).
                CometLoggerCallback(
                    project_name=comet_project,
                    save_checkpoints=True,
                )
            ]
        ),
    )
    result = trainer.fit()
    return result
Explanation: Now we define a simple training function. All the magic happens within the CometLoggerCallback:
python
CometLoggerCallback(
    project_name=comet_project,
    save_checkpoints=True,
)
It will automatically log all results to Comet ML and upload the checkpoints as artifacts. It assumes you're logged in into Comet via an API key or your ~./.comet.config.
End of explanation
comet_project = "ray_air_example"
train_dataset = get_train_dataset()
result = train_model(train_dataset=train_dataset, comet_project=comet_project)
Explanation: Let's kick off a run:
End of explanation
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