class_docstr: Base class for MCMC methods.
class_docstr_tok: ["Base","class","for","MCMC","methods","."]
code: def _validate_initial_params(self, initial_params):
      for v in initial_params.values():
          if v.shape[0] != self.num_chains:
              raise ValueError(
                  "The leading dimension of tensors in `initial_params` "
                  "must match the number of chains."
              )
  
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 6 for calling the AbstractMCMC obj's underlying member method code with example usage: obj._validate_initial_params(initial_params) without return types