class_docstr: Apply inverse CDF transform to Y.
  
  This means that we model uniform distributions as gaussian-distributed.
class_docstr_tok: ["Apply","inverse","CDF","transform","to","Y",".","This","means","that","we","model","uniform","distributions","as","gaussian-distributed","."]
docstr: Map to inverse Gaussian CDF in place.
docstr_tok: ["Map","to","inverse","Gaussian","CDF","in","place","."]
code: def _transform_observation_data(
      self,
      observation_data: List[ObservationData],
  ) -> List[ObservationData]:
      """Map to inverse Gaussian CDF in place."""
      # TODO (jej): Transform covariances.
      for obsd in observation_data:
          for idx, _ in enumerate(obsd.metric_names):
              mean = float(obsd.means[idx])
              # Error on out-of-domain values.
              if mean <= 0.0 or mean >= 1.0:
                  raise ValueError(
                      f"Inverse CDF cannot transform value: {mean} outside (0, 1)"
                  )
              var = float(obsd.covariance[idx, idx])
              (
                  transformed_mean,
                  transformed_var,
              ) = match_ci_width_truncated(
                  mean, var, self._map, lower_bound=0.0, upper_bound=1.0
              )
              obsd.means[idx] = transformed_mean
              obsd.covariance[idx, idx] = transformed_var
      return observation_data
  
file_path: Ax/ax/modelbridge/transforms/inverse_gaussian_cdf_y.py
imports: from logging import Logger
  from typing import List, Optional, TYPE_CHECKING
  from ax.core.observation import Observation, ObservationData
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.utils import match_ci_width_truncated
  from ax.models.types import TConfig
  from ax.utils.common.logger import get_logger
  from scipy.stats import norm
label_desc: Use image node_id 2 for calling the InverseGaussianCdfY obj's underlying member method code with example usage: obj._transform_observation_data(observation_data) and returns: observation_data