code: def _get_batched_jittered_initial_points(
      model: Model,
      chains: int,
      initvals: Optional[
          Union[StartDict, Sequence[Optional[StartDict]]]
      ],
      random_seed: RandomSeed,
      jitter: bool = True,
      jitter_max_retries: int = 10,
  ) -> Union[np.ndarray, List[np.ndarray]]:
      """Get jittered initial point in format expected by NumPyro MCMC kernel
  
      Returns
      -------
      out: list of ndarrays
          list with one item per variable and number of chains as batch dimension.
          Each item has shape `(chains, *var.shape)`
      """
  
      initial_points = _init_jitter(
          model,
          initvals,
          seeds=_get_seeds_per_chain(random_seed, chains),
          jitter=jitter,
          jitter_max_retries=jitter_max_retries,
      )
      initial_points_values = [
          list(initial_point.values())
          for initial_point in initial_points
      ]
      if chains == 1:
          return initial_points_values[0]
      return [
          np.stack(init_state)
          for init_state in zip(*initial_points_values)
      ]
  
imports: import logging
  import os
  import re
  from datetime import datetime
  from functools import partial
  from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
  import arviz
  import jax
  import jax.numpy
  import numpy
  import pytensor.tensor
  from arviz.data.base import make_attrs
  from jax.lax import scan
  from pytensor.compile import SharedVariable, Supervisor, mode
  from pytensor.graph.basic import graph_inputs
  from pytensor.graph.fg import FunctionGraph
  from pytensor.graph.replace import clone_replace
  from pytensor.link.jax.dispatch import jax_funcify
  from pytensor.raise_op import Assert
  from pytensor.tensor import TensorVariable
  from pytensor.tensor.random.type import RandomType
  from pymc import Model, modelcontext
  from pymc.backends.arviz import coords_and_dims_for_inferencedata, find_constants, find_observations
  from pymc.distributions.multivariate import PosDefMatrix
  from pymc.initial_point import StartDict
  from pymc.logprob.utils import CheckParameterValue
  from pymc.sampling.mcmc import _init_jitter
  from pymc.util import RandomSeed, RandomState, _get_seeds_per_chain, get_default_varnames
label_desc: Use image node_id 11 for calling a global function with example usage: _get_batched_jittered_initial_points(model, chains, initvals, random_seed, jitter, jitter_max_retries) and returns: unknown, initial_points_values