code: def _init_jitter(
      model: Model,
      initvals: Optional[
          Union[StartDict, Sequence[Optional[StartDict]]]
      ],
      seeds: Union[Sequence[int], np.ndarray],
      jitter: bool,
      jitter_max_retries: int,
  ) -> List[PointType]:
      """Apply a uniform jitter in [-1, 1] to the test value as starting point in each chain.
  
      ``model.check_start_vals`` is used to test whether the jittered starting
      values produce a finite log probability. Invalid values are resampled
      unless `jitter_max_retries` is achieved, in which case the last sampled
      values are returned.
  
      Parameters
      ----------
      jitter: bool
          Whether to apply jitter or not.
      jitter_max_retries : int
          Maximum number of repeated attempts at initializing values (per chain).
  
      Returns
      -------
      start : ``pymc.model.Point``
          Starting point for sampler
      """
  
      ipfns = make_initial_point_fns_per_chain(
          model=model,
          overrides=initvals,
          jitter_rvs=set(model.free_RVs) if jitter else set(),
          chains=len(seeds),
      )
  
      if not jitter:
          return [ipfn(seed) for ipfn, seed in zip(ipfns, seeds)]
  
      initial_points = []
      for ipfn, seed in zip(ipfns, seeds):
          rng = np.random.RandomState(seed)
          for i in range(jitter_max_retries + 1):
              point = ipfn(seed)
              if i < jitter_max_retries:
                  try:
                      model.check_start_vals(point)
                  except SamplingError:
                      # Retry with a new seed
                      seed = rng.randint(2**30, dtype=np.int64)
                  else:
                      break
          initial_points.append(point)
      return initial_points
  
imports: import logging
  import pickle
  import sys
  import time
  import warnings
  from typing import Any, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Set, Tuple, Type, Union, overload
  import numpy
  import pytensor.gradient
  from arviz import InferenceData, dict_to_dataset
  from arviz.data.base import make_attrs
  from fastprogress.fastprogress import progress_bar
  from pytensor.graph.basic import Variable
  from typing_extensions import Protocol, TypeAlias
  import pymc
  from pymc.backends import RunType, TraceOrBackend, init_traces
  from pymc.backends.arviz import coords_and_dims_for_inferencedata, find_constants, find_observations
  from pymc.backends.base import IBaseTrace, MultiTrace, _choose_chains
  from pymc.blocking import DictToArrayBijection
  from pymc.exceptions import SamplingError
  from pymc.initial_point import PointType, StartDict, make_initial_point_fns_per_chain
  from pymc.model import Model, modelcontext
  from pymc.sampling.parallel import Draw, _cpu_count
  from pymc.sampling.population import _sample_population
  from pymc.stats.convergence import log_warning_stats, log_warnings, run_convergence_checks
  from pymc.step_methods import NUTS, CompoundStep
  from pymc.step_methods.arraystep import BlockedStep, PopulationArrayStepShared
  from pymc.step_methods.hmc import quadpotential
  from pymc.util import RandomSeed, RandomState, _get_seeds_per_chain, drop_warning_stat, get_untransformed_name, is_transformed_name
  from pymc.vartypes import discrete_types
label_desc: Use image node_id 15 for calling a global function with example usage: _init_jitter(model, initvals, seeds, jitter, jitter_max_retries) and returns: initial_points, unknown