code: def __init__(
      self,
      chain_id,
      result_queue,
      log_queue,
      event,
      kernel,
      num_samples,
      warmup_steps,
      initial_params=None,
      hook=None,
  ):
      self.chain_id = chain_id
      self.kernel = kernel
      if initial_params is not None:
          self.kernel.initial_params = initial_params
      self.num_samples = num_samples
      self.warmup_steps = warmup_steps
      self.rng_seed = (torch.initial_seed() + chain_id) % MAX_SEED
      self.log_queue = log_queue
      self.result_queue = result_queue
      self.default_dtype = torch.Tensor().dtype
      self.default_device = torch.Tensor().device
      self.hook = hook
      self.event = event
  
imports: import copy
  import json
  import logging
  import queue
  import signal
  import threading
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.multiprocessing
  import pyro
  import pyro.poutine
  from pyro.infer.mcmc.hmc import HMC
  from pyro.infer.mcmc.logger import DIAGNOSTIC_MSG, ProgressBar, TqdmHandler, initialize_logger
  from pyro.infer.mcmc.nuts import NUTS
  from pyro.infer.mcmc.util import diagnostics, diagnostics_from_stats, print_summary, select_samples
  from pyro.ops.streaming import CountMeanVarianceStats, StatsOfDict, StreamingStats
  from pyro.util import optional
label_desc: Use image node_id 1 to create a new _Worker object  with example: obj = _Worker(chain_id, result_queue, log_queue, event, kernel, num_samples, warmup_steps, initial_params, hook)