code: def action_feature_config(self) -> rlt.ModelFeatureConfig:
      assert (
          len(self.action_float_features) > 0
      ), "You must set action_float_features"
      return get_feature_config(self.action_float_features)
  
imports: import logging
  from dataclasses import replace
  from typing import Dict, List, Optional, Tuple
  import reagent.core.types
  import torch
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.parameters import EvaluationParameters, NormalizationData, NormalizationKey
  from reagent.data import DataFetcher, ManualDataModule, ReAgentDataModule
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.model_managers.model_manager import ModelManager
  from reagent.models.model_feature_config_provider import RawModelFeatureConfigProvider
  from reagent.preprocessing.batch_preprocessor import BatchPreprocessor, PolicyNetworkBatchPreprocessor, Preprocessor
  from reagent.preprocessing.normalization import get_feature_config
  from reagent.preprocessing.types import InputColumn
  from reagent.reporting.actor_critic_reporter import ActorCriticReporter
  from reagent.training import ReAgentLightningModule
  from reagent.workflow.identify_types_flow import identify_normalization_parameters
  from reagent.workflow.types import Dataset, ModelFeatureConfigProvider__Union, PreprocessingOptions, ReaderOptions, ResourceOptions, RewardOptions, RLTrainingOutput, TableSpec
label_desc: Use image node_id 4 for calling the ActorCriticBase obj's underlying member method code with example usage: obj.action_feature_config() and returns: get_feature_config