class_docstr: Lazy representation of :func:`sequential_sum_product` .
  
  :param AssociativeOp sum_op: A marginalization op.
  :param AssociativeOp prod_op: A Bayesian fusion op.
  :param Funsor trans: A sequence of transition factors,
      usually varying along the ``time`` input.
  :param time: A time dimension.
  :type time: str or Variable
  :param dict step: A str-to-str mapping of "previous" inputs of ``trans``
      to "current" inputs of ``trans``.
  :param dict step_names: Optional, for internal use by alpha conversion.
code: def eager_subs(self, subs):
      assert isinstance(subs, tuple)
      # Eagerly rename variables.
      rename = {k: v.name for k, v in subs if isinstance(v, Variable)}
      if not rename:
          return None
      step_names = frozenset(
          (k, rename.get(v, v)) for k, v in self.step_names.items()
      )
      result = MarkovProduct(
          self.sum_op,
          self.prod_op,
          self.trans,
          self.time,
          self.step,
          step_names,
      )
      lazy = tuple(
          (k, v) for k, v in subs if not isinstance(v, Variable)
      )
      if lazy:
          result = Subs(result, lazy)
      return result
  
imports: import re
  from collections import OrderedDict, defaultdict
  from functools import reduce
  from math import gcd
  import funsor
  import funsor.ops
  from funsor.cnf import Contraction
  from funsor.domains import Bint, Reals
  from funsor.interpreter import gensym
  from funsor.ops import PRODUCT_TO_POWER, UNITS, AssociativeOp
  from funsor.terms import Cat, Funsor, FunsorMeta, Number, Slice, Stack, Subs, Variable, eager, substitute, to_funsor
  from funsor.util import quote
label_desc: Use image node_id 3 for calling the MarkovProduct obj's underlying member method code with example usage: obj.eager_subs(subs) and returns: result, None