class_docstr: Wraps population of step methods to step them in parallel with single or multiprocessing.
class_docstr_tok: ["Wraps","population","of","step","methods","to","step","them","in","parallel","with","single","or","multiprocessing","."]
docstr: Do nothing: processes are already started in ``__init__``.
docstr_tok: ["Do","nothing",":","processes","are","already","started","in","``","__init__","``","."]
code: def __enter__(self):
      """Do nothing: processes are already started in ``__init__``."""
      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 2 for calling the PopulationStepper obj's underlying member method code with example usage: obj.__enter__() without return types