class_docstr: Blocked step method that is generalized to accept vectors of variables.
  
  Parameters
  ----------
  vars: list
      List of value variables for sampler.
  fs: list of logp PyTensor functions
  allvars: Boolean (default False)
  blocked: Boolean (default True)
class_docstr_tok: ["Blocked","step","method","that","is","generalized","to","accept","vectors","of","variables",".","Parameters","--","--","--","--","--","vars",":","list","List","of","value","variables","for","sampler",".","fs",":","list","of","logp","PyTensor","functions","allvars",":","Boolean","(","default","False",")","blocked",":","Boolean","(","default","True",")"]
code: def __init__(self, vars, fs, allvars=False, blocked=True):
      self.vars = vars
      self.fs = fs
      self.allvars = allvars
      self.blocked = blocked
  
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 to create a new ArrayStep object from inherited base classes: BlockedStep with example: obj = ArrayStep(vars, fs, allvars, blocked)