class_docstr: Managed optimization loop, in which Ax oversees deployment of trials and
  gathering data.
class_docstr_tok: ["Managed","optimization","loop",",","in","which","Ax","oversees","deployment","of","trials","and","gathering","data","."]
docstr: Constructs an asynchronous `OptimizationLoop` using Ax runners and
  metrics.
docstr_tok: ["Constructs","an","asynchronous","`","OptimizationLoop","`","using","Ax","runners","and","metrics","."]
code: def with_runners_and_metrics(
      cls,
      parameters: List[TParameterRepresentation],
      path_to_runner: str,
      paths_to_metrics: List[str],
      experiment_name: Optional[str] = None,
      objective_name: Optional[str] = None,
      minimize: bool = False,
      parameter_constraints: Optional[List[str]] = None,
      outcome_constraints: Optional[List[str]] = None,
      total_trials: int = 20,
      arms_per_trial: int = 1,
      wait_time: int = 0,
      random_seed: Optional[int] = None,
  ) -> "OptimizationLoop":
      """Constructs an asynchronous `OptimizationLoop` using Ax runners and
      metrics."""
      # NOTE: Could use `Scheduler` to implement this if needed.
      raise NotImplementedError
  
imports: from __future__ import annotations
  import inspect
  import logging
  from typing import Iterable, List, Optional, Tuple
  from ax.core.arm import Arm
  from ax.core.base_trial import BaseTrial
  from ax.core.batch_trial import BatchTrial
  from ax.core.experiment import Experiment
  from ax.core.formatting_utils import data_and_evaluations_from_raw_data
  from ax.core.trial import Trial
  from ax.core.types import TEvaluationFunction, TEvaluationOutcome, TModelPredictArm, TParameterization
  from ax.core.utils import get_pending_observation_features
  from ax.exceptions.constants import CHOLESKY_ERROR_ANNOTATION
  from ax.exceptions.core import SearchSpaceExhausted, UserInputError
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.dispatch_utils import choose_generation_strategy
  from ax.modelbridge.generation_strategy import GenerationStrategy
  from ax.modelbridge.registry import Models
  from ax.service.utils.best_point import get_best_parameters_from_model_predictions, get_best_raw_objective_point
  from ax.service.utils.instantiation import InstantiationBase, TParameterRepresentation
  from ax.utils.common.executils import retry_on_exception
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import not_none
label_desc: Use image node_id 3 for calling the OptimizationLoop obj's underlying member method code with example usage: obj.with_runners_and_metrics(cls, parameters, path_to_runner, paths_to_metrics, experiment_name, objective_name, minimize, parameter_constraints, outcome_constraints, total_trials, arms_per_trial, wait_time, random_seed) without return types