class_docstr: Algorithm framework with 3 main functions:
  
  - :code:`ask()` which provides a candidate on which to evaluate the function to optimize.
  - :code:`tell(candidate, loss)` which lets you provide the loss associated to points.
  - :code:`provide_recommendation()` which provides the best final candidate.
  
  Typically, one would call :code:`ask()` num_workers times, evaluate the
  function on these num_workers points in parallel, update with the fitness value when the
  evaluations is finished, and iterate until the budget is over. At the very end,
  one would call provide_recommendation for the estimated optimum.
  
  This class is abstract, it provides internal equivalents for the 3 main functions,
  among which at least :code:`_internal_ask_candidate` has to be overridden.
  
  Each optimizer instance should be used only once, with the initial provided budget
  
  Parameters
  ----------
  parametrization: int or Parameter
      either the dimension of the optimization space, or its parametrization
  budget: int/None
      number of allowed evaluations
  num_workers: int
      number of evaluations which will be run in parallel at once
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docstr: Optimization (minimization) procedure
  
  Parameters
  ----------
  objective_function: callable
      A callable to optimize (minimize)
  executor: Executor
      An executor object, with method :code:`submit(callable, *args, **kwargs)` and returning a Future-like object
      with methods :code:`done() -> bool` and :code:`result() -> float`. The executor role is to dispatch the execution of
      the jobs locally/on a cluster/with multithreading depending on the implementation.
      Eg: :code:`concurrent.futures.ProcessPoolExecutor`
  batch_mode: bool
      when :code:`num_workers = n > 1`, whether jobs are executed by batch (:code:`n` function evaluations are launched,
      we wait for all results and relaunch n evals) or not (whenever an evaluation is finished, we launch
      another one)
  verbosity: int
      print information about the optimization (0: None, 1: fitness values, 2: fitness values and recommendation)
  constraint_violation: list of functions or None
      each function in the list returns >0 for a violated constraint.
  
  Returns
  -------
  ng.p.Parameter
      The candidate with minimal value. :code:`ng.p.Parameters` have field :code:`args` and :code:`kwargs` which can
      be directly used on the function (:code:`objective_function(*candidate.args, **candidate.kwargs)`).
  
  Note
  ----
  for evaluation purpose and with the current implementation, it is better to use batch_mode=True
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code: def minimize(
      self,
      objective_function: tp.Callable[..., tp.Loss],
      executor: tp.Optional[tp.ExecutorLike] = None,
      batch_mode: bool = False,
      verbosity: int = 0,
      constraint_violation: tp.Any = None,
  ) -> p.Parameter:
      """Optimization (minimization) procedure
  
      Parameters
      ----------
      objective_function: callable
          A callable to optimize (minimize)
      executor: Executor
          An executor object, with method :code:`submit(callable, *args, **kwargs)` and returning a Future-like object
          with methods :code:`done() -> bool` and :code:`result() -> float`. The executor role is to dispatch the execution of
          the jobs locally/on a cluster/with multithreading depending on the implementation.
          Eg: :code:`concurrent.futures.ProcessPoolExecutor`
      batch_mode: bool
          when :code:`num_workers = n > 1`, whether jobs are executed by batch (:code:`n` function evaluations are launched,
          we wait for all results and relaunch n evals) or not (whenever an evaluation is finished, we launch
          another one)
      verbosity: int
          print information about the optimization (0: None, 1: fitness values, 2: fitness values and recommendation)
      constraint_violation: list of functions or None
          each function in the list returns >0 for a violated constraint.
  
      Returns
      -------
      ng.p.Parameter
          The candidate with minimal value. :code:`ng.p.Parameters` have field :code:`args` and :code:`kwargs` which can
          be directly used on the function (:code:`objective_function(*candidate.args, **candidate.kwargs)`).
  
      Note
      ----
      for evaluation purpose and with the current implementation, it is better to use batch_mode=True
      """
      # pylint: disable=too-many-branches
      if self.budget is None:
          raise ValueError("Budget must be specified")
      if executor is None:
          executor = (
              utils.SequentialExecutor()
          )  # defaults to run everything locally and sequentially
          if self.num_workers > 1:
              self._warn(
                  f"num_workers = {self.num_workers} > 1 is suboptimal when run sequentially",
                  errors.InefficientSettingsWarning,
              )
      assert executor is not None
      tmp_runnings: tp.List[
          tp.Tuple[p.Parameter, tp.JobLike[tp.Loss]]
      ] = []
      tmp_finished: tp.Deque[
          tp.Tuple[p.Parameter, tp.JobLike[tp.Loss]]
      ] = deque()
      # go
      sleeper = (
          ngtools.Sleeper()
      )  # manages waiting time depending on execution time of the jobs
      remaining_budget = self.budget - self.num_ask
      first_iteration = True
      #
      while (
          remaining_budget or self._running_jobs or self._finished_jobs
      ):
          # # # # # Update optimizer with finished jobs # # # # #
          # this is the first thing to do when resuming an existing optimization run
          # process finished
          if self._finished_jobs:
              if (
                  remaining_budget or sleeper._start is not None
              ) and not first_iteration:
                  # ignore stop if no more suggestion is sent
                  # this is an ugly hack to avoid warnings at the end of steady mode
                  sleeper.stop_timer()
              while self._finished_jobs:
                  x, job = self._finished_jobs[0]
                  result = job.result()
                  if constraint_violation is not None:
                      self.tell(
                          x,
                          result,
                          [f(x.value) for f in constraint_violation],
                      )  # TODO: this is not parallelized, wtf!
                  else:
                      self.tell(x, result)
                  self._finished_jobs.popleft()  # remove it after the tell to make sure it was indeed "told" (in case of interruption)
                  if verbosity:
                      print(
                          f"Updating fitness with value {job.result()}"
                      )
              if verbosity:
                  print(
                      f"{remaining_budget} remaining budget and {len(self._running_jobs)} running jobs"
                  )
                  if verbosity > 1:
                      print(
                          "Current pessimistic best is: {}".format(
                              self.current_bests["pessimistic"]
                          )
                      )
          elif not first_iteration:
              sleeper.sleep()
          # # # # # Start new jobs # # # # #
          if not batch_mode or not self._running_jobs:
              new_sugg = max(
                  0,
                  min(
                      remaining_budget,
                      self.num_workers - len(self._running_jobs),
                  ),
              )
              if verbosity and new_sugg:
                  print(
                      f"Launching {new_sugg} jobs with new suggestions"
                  )
              for _ in range(new_sugg):
                  try:
                      args = self.ask()
                  except errors.NevergradEarlyStopping:
                      remaining_budget = 0
                      break
                  self._running_jobs.append(
                      (
                          args,
                          executor.submit(
                              objective_function,
                              *args.args,
                              **args.kwargs,
                          ),
                      )
                  )
              if new_sugg:
                  sleeper.start_timer()
          if remaining_budget > 0:  # early stopping sets it to 0
              remaining_budget = self.budget - self.num_ask
          # split (repopulate finished and runnings in only one loop to avoid
          # weird effects if job finishes in between two list comprehensions)
          tmp_runnings, tmp_finished = [], deque()
          for x_job in self._running_jobs:
              (
                  tmp_finished if x_job[1].done() else tmp_runnings
              ).append(x_job)
          self._running_jobs, self._finished_jobs = (
              tmp_runnings,
              tmp_finished,
          )
          first_iteration = False
      return (
          self.provide_recommendation()
          if self.num_objectives == 1
          else p.Constant(None)
      )
  
imports: import pickle
  import warnings
  from pathlib import Path
  from numbers import Real
  from collections import deque
  import numpy
  import nevergrad.common.typing
  from nevergrad.parametrization import parameter
  from nevergrad.common import tools
  from nevergrad.common import errors
  from nevergrad.common.decorators import Registry
  from .None import utils
  from .None import multiobjective
label_desc: Use image node_id 31 for calling the Optimizer obj's underlying member method code with example usage: obj.minimize(objective_function, executor, batch_mode, verbosity, constraint_violation) and returns: unknown