class_docstr: Defines the API for a transform that is applied to search_space,
  observation_features, observation_data, and optimization_config.
  
  Transforms are used to adapt the search space and data into the types
  and structures expected by the model. When Transforms are used (for
  instance, in ModelBridge), it is always assumed that they may potentially
  mutate the transformed object in-place.
  
  Forward transforms are defined for all four of those quantities. Reverse
  transforms are defined for observation_data and observation.
  
  The forward transform for observation features must accept a partial
  observation with not all features recorded.
  
  Forward and reverse transforms for observation data accept a list of
  observation features as an input, but they will not be mutated.
  
  The forward transform for optimization config accepts the modelbridge and
  fixed features as inputs, but they will not be mutated.
  
  This class provides an identify transform.
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docstr: Transform search space.
  
  The transforms are typically done in-place. This calls two private methods,
  `_transform_search_space`, which transforms the core search space attributes,
  and `_transform_parameter_distributions`, which transforms the distributions
  when using a `RobustSearchSpace`.
  
  Args:
      search_space: The search space
  
  Returns: transformed search space.
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code: def transform_search_space(
      self, search_space: SearchSpace
  ) -> SearchSpace:
      """Transform search space.
  
      The transforms are typically done in-place. This calls two private methods,
      `_transform_search_space`, which transforms the core search space attributes,
      and `_transform_parameter_distributions`, which transforms the distributions
      when using a `RobustSearchSpace`.
  
      Args:
          search_space: The search space
  
      Returns: transformed search space.
      """
      self._transform_parameter_distributions(search_space=search_space)
      return self._transform_search_space(search_space=search_space)
  
imports: from __future__ import annotations
  from typing import List, Optional, TYPE_CHECKING
  from ax.core.observation import Observation, ObservationData, ObservationFeatures, separate_observations
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.outcome_constraint import OutcomeConstraint
  from ax.core.search_space import RobustSearchSpace, SearchSpace
  from ax.exceptions.core import UnsupportedError
  from ax.models.types import TConfig
label_desc: Use image node_id 2 for calling the Transform obj's underlying member method code with example usage: obj.transform_search_space(search_space) and returns: self