code: def _compute_power_transforms(
      Ys: Dict[str, List[float]]
  ) -> Dict[str, PowerTransformer]:
      """Compute power transforms."""
      power_transforms = {}
      for k, ys in Ys.items():
          y = np.array(ys)[
              :, None
          ]  # Need to unsqueeze the last dimension
          pt = PowerTransformer(method="yeo-johnson").fit(y)
          power_transforms[k] = pt
      return power_transforms
  
imports: from __future__ import annotations
  from collections import defaultdict
  from logging import Logger
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.outcome_constraint import OutcomeConstraint, ScalarizedOutcomeConstraint
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.utils import get_data, match_ci_width_truncated
  from ax.models.types import TConfig
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast_list
  from sklearn.preprocessing import PowerTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: _compute_power_transforms(Ys) and returns: power_transforms