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","."]
code: def __init__(
      self,
      search_space: Optional[SearchSpace] = None,
      observations: Optional[List[Observation]] = None,
      modelbridge: Optional["base_modelbridge.ModelBridge"] = None,
      config: Optional[TConfig] = None,
  ) -> None:
      # pyre-fixme[4]: Attribute must be annotated.
      self.dist = norm(loc=0, scale=1)
  
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 1 to create a new InverseGaussianCdfY object from inherited base classes: Transform with example: obj = InverseGaussianCdfY(search_space, observations, modelbridge, config)