code: def constraint_satisfaction(trial: BaseTrial) -> bool:
      """
      This function checks whether the outcome constraints of the
      optimization config of an experiment are satisfied in the
      given trial.
  
      Args:
          trial: A single-arm Trial at which we want to check the constraint.
  
      Returns:
          A boolean which is True iff all outcome constraints are satisfied.
      """
      outcome_constraints = not_none(
          trial.experiment.optimization_config
      ).outcome_constraints
      if len(outcome_constraints) == 0:
          return True
  
      df = trial.lookup_data().df
      for constraint in outcome_constraints:
          bound = constraint.bound
          metric_name = constraint.metric.name
          metric_data = df.loc[df["metric_name"] == metric_name]
          mean, sem = metric_data.iloc[0][["mean", "sem"]]
          if sem > 0.0:
              logger.warning(
                  f"There is observation noise for metric {metric_name}. This may "
                  "negatively affect the way we check constraint satisfaction."
              )
          if constraint.op is ComparisonOp.LEQ:
              satisfied = mean <= bound
          else:
              satisfied = mean >= bound
          if not satisfied:
              return False
  
      return True
  
imports: from logging import Logger
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  from ax.core.base_trial import BaseTrial, TrialStatus
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ObjectiveThreshold
  from ax.core.trial import Trial
  from ax.core.types import ComparisonOp
  from ax.global_stopping.strategies.base import BaseGlobalStoppingStrategy
  from ax.modelbridge.modelbridge_utils import observed_hypervolume
  from ax.plot.pareto_utils import get_tensor_converter_model
  from ax.service.utils.best_point import fill_missing_thresholds_from_nadir
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
label_desc: Use image node_id 1 for calling a global function with example usage: constraint_satisfaction(trial) and returns: True, True, False