code: def _sample_population(
      *,
      initial_points: Sequence[PointType],
      draws: int,
      start: Sequence[PointType],
      random_seed: RandomSeed,
      step: Union[BlockedStep, CompoundStep],
      tune: int,
      model: Model,
      progressbar: bool = True,
      parallelize: bool = False,
      traces: Sequence[BaseTrace],
      **kwargs,
  ):
      """Performs sampling of a population of chains using the ``PopulationStepper``.
  
      Parameters
      ----------
      draws : int
          The number of samples to draw
      start : list
          Start points for each chain
      random_seed : single random seed, optional
      step : function
          Step function (should be or contain a population step method)
      tune : int
          Number of iterations to tune.
      model : Model (optional if in ``with`` context)
      progressbar : bool
          Show progress bars? (defaults to True)
      parallelize : bool
          Setting for multiprocess parallelization
      """
      warn_population_size(
          step=step,
          initial_points=initial_points,
          model=model,
          chains=len(traces),
      )
  
      sampling = _prepare_iter_population(
          draws=draws,
          step=step,
          start=start,
          parallelize=parallelize,
          traces=traces,
          tune=tune,
          model=model,
          random_seed=random_seed,
          progressbar=progressbar,
      )
  
      if progressbar:
          sampling = progress_bar(
              sampling, total=draws, display=progressbar
          )
  
      for i in sampling:
          pass
      return
  
imports: import logging
  import warnings
  from copy import copy
  from typing import Iterator, List, Sequence, Tuple, Union
  import cloudpickle
  import numpy
  from fastprogress.fastprogress import progress_bar
  from typing_extensions import TypeAlias
  from pymc.backends.base import BaseTrace
  from pymc.initial_point import PointType
  from pymc.model import Model, modelcontext
  from pymc.stats.convergence import log_warning_stats
  from pymc.step_methods import CompoundStep
  from pymc.step_methods.arraystep import BlockedStep, PopulationArrayStepShared, StatsType
  from pymc.step_methods.metropolis import DEMetropolis
  from pymc.util import RandomSeed
label_desc: Use image node_id 1 for calling a global function with example usage: _sample_population() without return types