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","."]
code: def _get_new_trial(self) -> BaseTrial:
      if self.arms_per_trial == 1:
          return self.experiment.new_trial(
              generator_run=self.generation_strategy.gen(
                  experiment=self.experiment,
                  pending_observations=get_pending_observation_features(
                      experiment=self.experiment
                  ),
              )
          )
      elif self.arms_per_trial > 1:
          return self.experiment.new_batch_trial(
              generator_run=self.generation_strategy.gen(
                  experiment=self.experiment, n=self.arms_per_trial
              )
          )
      else:
          raise UserInputError(
              f"Invalid number of arms per trial: {self.arms_per_trial}"
          )
  
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 5 for calling the OptimizationLoop obj's underlying member method code with example usage: obj._get_new_trial() and returns: self, self