class_docstr: Standardize Y, separately for each metric.
  
  Transform is done in-place.
class_docstr_tok: ["Standardize","Y",",","separately","for","each","metric",".","Transform","is","done","in-place","."]
code: def untransform_outcome_constraints(
      self,
      outcome_constraints: List[OutcomeConstraint],
      fixed_features: Optional[ObservationFeatures] = None,
  ) -> List[OutcomeConstraint]:
      for c in outcome_constraints:
          if c.relative:
              raise ValueError(
                  f"StandardizeY transform does not support relative constraint {c}"
              )
          if isinstance(c, ScalarizedOutcomeConstraint):
              raise ValueError(
                  "ScalarizedOutcomeConstraint not supported"
              )
          c.bound = float(
              c.bound * self.Ystd[c.metric.name]
              + self.Ymean[c.metric.name]
          )
      return outcome_constraints
  
imports: from logging import Logger
  from typing import DefaultDict, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  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.core.types import TParamValue
  from ax.exceptions.core import DataRequiredError
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.utils import get_data
  from ax.models.types import TConfig
  from ax.utils.common.logger import get_logger
label_desc: Use image node_id 5 for calling the StandardizeY obj's underlying member method code with example usage: obj.untransform_outcome_constraints(outcome_constraints, fixed_features) and returns: outcome_constraints