class_docstr: A map between the original and transformed registries.
  
  This object is used in the `SpaceTransform` to check if a trial in the original space
  has equivalent trials in the transformed space.
  
  The goal is to make it so the algorithms don't have to care about the transforms/etc.
class_docstr_tok: ["A","map","between","the","original","and","transformed","registries",".","This","object","is","used","in","the","`","SpaceTransform","`","to","check","if","a","trial","in","the","original","space","has","equivalent","trials","in","the","transformed","space",".","The","goal","is","to","make","it","so","the","algorithms","do","n't","have","to","care","about","the","transforms\/etc","."]
docstr: Register an equivalence between the given original trial and the transformed trial.
docstr_tok: ["Register","an","equivalence","between","the","given","original","trial","and","the","transformed","trial","."]
code: def register(
      self, original_trial: Trial, transformed_trial: Trial
  ) -> str:
      """Register an equivalence between the given original trial and the transformed trial."""
      # NOTE: Choosing not to register the trials here, and instead do it more manually.
      # original_id = self.original_registry.register(original_trial)
      # transformed_id = self.transformed_registry.register(transformed_trial)
      original_trial_id = _get_id(original_trial)
      transformed_trial_id = _get_id(transformed_trial)
      self._mapping[original_trial_id].add(transformed_trial_id)
      return original_trial_id
  
imports: from __future__ import annotations
  import copy
  from collections import defaultdict
  from logging import getLogger
  from typing import Any, Container, Iterable, Iterator, Mapping
  from orion.core.worker.trial import Trial, TrialCM
label_desc: Use image node_id 9 for calling the RegistryMapping obj's underlying member method code with example usage: obj.register(original_trial, transformed_trial) and returns: original_trial_id