class_docstr: Separate process for each chain.
  We communicate with the main process using a pipe,
  and send finished samples using shared memory.
class_docstr_tok: ["Separate","process","for","each","chain",".","We","communicate","with","the","main","process","using","a","pipe",",","and","send","finished","samples","using","shared","memory","."]
code: def __init__(
      self,
      name: str,
      msg_pipe,
      step_method,
      step_method_is_pickled,
      shared_point,
      draws: int,
      tune: int,
      seed,
  ):
      self._msg_pipe = msg_pipe
      self._step_method = step_method
      self._step_method_is_pickled = step_method_is_pickled
      self._shared_point = shared_point
      self._seed = seed
      self._at_seed = seed + 1
      self._draws = draws
      self._tune = tune
  
imports: import ctypes
  import logging
  import multiprocessing
  import multiprocessing.sharedctypes
  import platform
  import time
  import traceback
  from collections import namedtuple
  from typing import Dict, List, Sequence
  import cloudpickle
  import numpy
  from fastprogress.fastprogress import progress_bar
  from pymc.blocking import DictToArrayBijection
  from pymc.exceptions import SamplingError
  from pymc.util import RandomSeed
label_desc: Use image node_id 1 to create a new _Process object  with example: obj = _Process(name, msg_pipe, step_method, step_method_is_pickled, shared_point, draws, tune, seed)