code: def metrop_select(
      mr: np.ndarray, q: np.ndarray, q0: np.ndarray
  ) -> Tuple[np.ndarray, bool]:
      """Perform rejection/acceptance step for Metropolis class samplers.
  
      Returns the new sample q if a uniform random number is less than the
      metropolis acceptance rate (`mr`), and the old sample otherwise, along
      with a boolean indicating whether the sample was accepted.
  
      Parameters
      ----------
      mr: float, Metropolis acceptance rate
      q: proposed sample
      q0: current sample
  
      Returns
      -------
      q or q0
      """
      # Compare acceptance ratio to uniform random number
      # TODO XXX: This `uniform` is not given a model-specific RNG state, which
      # means that sampler runs that use it will not be reproducible.
      if np.isfinite(mr) and np.log(uniform()) < mr:
          return q, True
      else:
          return q0, False
  
imports: from abc import abstractmethod
  from typing import Callable, List, Tuple, Union, cast
  import numpy
  from numpy.random import uniform
  from pymc.blocking import DictToArrayBijection, PointType, RaveledVars, StatsType
  from pymc.model import modelcontext
  from pymc.step_methods.compound import BlockedStep
  from pymc.util import get_var_name
label_desc: Use image node_id 1 for calling a global function with example usage: metrop_select(mr, q, q0) and returns: q,  True, q0,  False