code: def _objective_threshold_from_nadir(
      experiment: Experiment,
      objective: Objective,
      optimization_config: Optional[
          MultiObjectiveOptimizationConfig
      ] = None,
  ) -> ObjectiveThreshold:
      """
      Find the worst value observed for each objective and create an ObjectiveThreshold
      with this as the bound.
      """
  
      logger.info(
          f"Inferring ObjectiveThreshold for {objective} using nadir point."
      )
  
      optimization_config = optimization_config or checked_cast(
          MultiObjectiveOptimizationConfig,
          experiment.optimization_config,
      )
  
      data_df = experiment.fetch_data().df
  
      mean = data_df[data_df["metric_name"] == objective.metric.name][
          "mean"
      ]
      bound = max(mean) if objective.minimize else min(mean)
      op = ComparisonOp.LEQ if objective.minimize else ComparisonOp.GEQ
  
      return ObjectiveThreshold(
          metric=objective.metric, bound=bound, op=op, relative=False
      )
  
imports: from collections import OrderedDict
  from functools import reduce
  from logging import Logger
  from typing import Dict, Iterable, List, Optional, Tuple, Type
  import pandas
  import torch
  from ax.core.base_trial import TrialStatus
  from ax.core.batch_trial import BatchTrial
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.generator_run import GeneratorRun
  from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
  from ax.core.observation import Observation
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ObjectiveThreshold, OutcomeConstraint
  from ax.core.trial import Trial
  from ax.core.types import ComparisonOp, TModelPredictArm, TParameterization
  from ax.exceptions.core import UnsupportedError, UserInputError
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.cross_validation import assess_model_fit, compute_diagnostics, cross_validate
  from ax.modelbridge.generation_strategy import GenerationStrategy
  from ax.modelbridge.modelbridge_utils import observed_pareto_frontier, predicted_pareto_frontier
  from ax.modelbridge.registry import get_model_from_generator_run, ModelRegistryBase, Models
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.utils import derelativize_optimization_config_with_raw_status_quo
  from ax.plot.pareto_utils import get_tensor_converter_model
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
  from numpy import NaN
  from torch import Tensor
label_desc: Use image node_id 19 for calling a global function with example usage: _objective_threshold_from_nadir(experiment, objective, optimization_config) and returns: ObjectiveThreshold