code: def __post_init_post_parse__(self):
      super().__post_init_post_parse__()
      assert (
          self.state_preprocessing_options is None
          or self.state_preprocessing_options.allowedlist_features
          is None
      ), (
          "Please set state allowlist features in state_float_features field of "
          "config instead"
      )
      assert (
          self.item_preprocessing_options is None
          or self.item_preprocessing_options.allowedlist_features
          is None
      ), (
          "Please set item allowlist features in item_float_features field of "
          "config instead"
      )
      assert (
          self.item_preprocessing_options is None
          or self.item_preprocessing_options.sequence_feature_id is None
      ), "Please set slate_feature_id field of config instead"
      self._state_preprocessing_options = (
          self.state_preprocessing_options
      )
      self._item_preprocessing_options = self.item_preprocessing_options
      self.eval_parameters = self.trainer_param.evaluation
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import reagent.core.types
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import NormalizationData
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.samplers.top_k_sampler import TopKSampler
  from reagent.gym.policies.scorers.slate_q_scorer import slate_q_scorer
  from reagent.model_managers.model_manager import ModelManager
  from reagent.preprocessing.normalization import get_feature_config
  from reagent.reporting.slate_q_reporter import SlateQReporter
  from reagent.training import ReAgentLightningModule
  from reagent.workflow.types import PreprocessingOptions
label_desc: Use image node_id 1 for calling the SlateQBase obj's underlying member method code with example usage: obj.__post_init_post_parse__() without return types