code: def _get_recsim_state(self, observation):
      def _stack(slates):
          obs = rlt.FeatureData(
              float_features=torch.from_numpy(
                  np.stack(
                      np.array([slate["user"] for slate in slates])
                  )
              ),
              candidate_docs=rlt.DocList(
                  float_features=torch.from_numpy(
                      np.stack(
                          np.array([slate["doc"] for slate in slates])
                      )
                  )
              ),
          )
          return obs
  
      def _stack_slate(slate):
          return {
              "user": slate["user"],
              "doc": np.stack(np.array(list(slate["doc"].values()))),
          }
  
      return _stack([_stack_slate(slate) for slate in observation])
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 3 for calling the PolicyGradientInputMaker obj's underlying member method code with example usage: obj._get_recsim_state(observation) and returns: _stack, obs, dict