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 _map(self, val: float) -> float:
      mapped_val = self.dist.ppf(val)
      return mapped_val
  
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 3 for calling the InverseGaussianCdfY obj's underlying member method code with example usage: obj._map(val) and returns: mapped_val