code: def _get_data(dataset) -> Dict[str, np.ndarray]:
      """Return sklearn dataset, loading and caching if necessary."""
      if dataset is SklearnDataset.DIGITS:
          return datasets.load_digits()
      elif dataset is SklearnDataset.BOSTON:
          return datasets.load_boston()
      elif dataset is SklearnDataset.CANCER:
          return datasets.load_breast_cancer()
      else:
          raise NotImplementedError(
              f"{dataset.value} is not a currently supported {dataset.name}."
          )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from enum import Enum
  from functools import lru_cache
  from math import sqrt
  from typing import Any, Dict, Tuple
  import numpy
  import pandas
  from ax.core.arm import Arm
  from ax.core.base_trial import BaseTrial
  from ax.core.data import Data
  from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
  from ax.utils.common.result import Err, Ok
  from ax.utils.common.typeutils import checked_cast
  from sklearn import datasets
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.model_selection import cross_val_score
  from sklearn.neural_network import MLPClassifier, MLPRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: _get_data(dataset) and returns: datasets, datasets, datasets