code: def get_dataset():
      ds1 = ray.data.read_csv(
          "s3://anonymous@air-example-data/breast_cancer.csv"
      )
      prep_v1 = StandardScaler(["worst radius", "worst area"])
      ds1 = prep_v1.fit_transform(ds1)
      return ds1
  
imports: import ray
  from ray import tune
  from ray.tune import Tuner
  from ray.train.xgboost import XGBoostTrainer
  import ray
  from ray import tune
  from ray.tune import Tuner
  from ray.train.xgboost import XGBoostTrainer
  from ray.train import ScalingConfig, RunConfig
  import os
  from ray import tune
  from ray.tune import Tuner
  from ray.train.examples.pytorch.torch_linear_example import train_func
  from ray.train.torch import TorchTrainer
  from ray.data.preprocessors import StandardScaler
  from ray.tune.search.bayesopt import BayesOptSearch
  from ray.tune.schedulers import HyperBandScheduler
  from ray.tune import TuneConfig
  from ray.tune import Tuner, TuneConfig
  from ray.train import CheckpointConfig, RunConfig
  from ray.tune import TuneConfig
  from ray.tune.search.bayesopt import BayesOptSearch
label_desc: Use image node_id 1 for calling a global function with example usage: get_dataset() and returns: ds1