code: def update(self, state: State, actions: Sequence[float]) -> float:
      old_dist = self._uniform_probs
      if state in self._state_space:
          old_dist = self._state_space[state]
      self._state_space[state] = actions
      return (
          reduce(
              lambda a, b: a + b,
              map(lambda a: (a[0] - a[1]) ** 2, zip(old_dist, actions)),
          )
          ** 0.5
      )
  
imports: import pickle
  from functools import reduce
  from typing import List, Mapping, Sequence
  import torch
  from reagent.ope.estimators.sequential_estimators import Model, RLPolicy, State, ValueFunction
  from reagent.ope.estimators.types import Action, ActionDistribution, ActionSpace
  from reagent.ope.test.envs import Environment, PolicyLogGenerator
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 2 for calling the TabularPolicy obj's underlying member method code with example usage: obj.update(state, actions) and returns: unknown