class_docstr: An optimization configuration for multi-objective optimization,
  which comprises multiple objective, outcome constraints, objective
  thresholds, and an optional risk measure.
  
  There is no minimum or maximum number of outcome constraints, but an
  individual metric can have at most two constraints--which is how we
  represent metrics with both upper and lower bounds.
  
  ObjectiveThresholds should be present for every objective. A good
  rule of thumb is to set them 10% below the minimum acceptable value
  for each metric.
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docstr: Get a mapping from objective metric name to the corresponding
  threshold.
docstr_tok: ["Get","a","mapping","from","objective","metric","name","to","the","corresponding","threshold","."]
code: def objective_thresholds_dict(self) -> Dict[str, ObjectiveThreshold]:
      """Get a mapping from objective metric name to the corresponding
      threshold.
      """
      return {ot.metric.name: ot for ot in self._objective_thresholds}
  
imports: from itertools import groupby
  from logging import Logger
  from typing import Dict, List, Optional
  from ax.core.metric import Metric
  from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
  from ax.core.outcome_constraint import ComparisonOp, ObjectiveThreshold, OutcomeConstraint, ScalarizedOutcomeConstraint
  from ax.core.risk_measures import RiskMeasure
  from ax.exceptions.core import UserInputError
  from ax.utils.common.base import Base
  from ax.utils.common.logger import get_logger
label_desc: Use image node_id 9 for calling the MultiObjectiveOptimizationConfig obj's underlying member method code with example usage: obj.objective_thresholds_dict() and returns: unknown