code: def query_data(
      self,
      input_table_spec: TableSpec,
      sample_range: Optional[Tuple[float, float]],
      reward_options: RewardOptions,
      data_fetcher: DataFetcher,
  ) -> Dataset:
      return data_fetcher.query_data_synthetic_reward(
          input_table_spec=input_table_spec,
          discrete_action_names=self.model_manager.discrete_action_names,
          sample_range=sample_range,
          max_seq_len=self.model_manager.max_seq_len,
      )
  
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, param_hash
  from reagent.data.data_fetcher import DataFetcher
  from reagent.data.manual_data_module import ManualDataModule
  from reagent.data.reagent_data_module import ReAgentDataModule
  from reagent.model_managers.model_manager import ModelManager
  from reagent.net_builder.synthetic_reward.single_step_synthetic_reward import SingleStepSyntheticReward
  from reagent.net_builder.unions import SyntheticRewardNetBuilder__Union
  from reagent.preprocessing.types import InputColumn
  from reagent.reporting.reward_network_reporter import RewardNetworkReporter
  from reagent.training import ReAgentLightningModule, RewardNetTrainer, RewardNetworkTrainerParameters
  from reagent.workflow.identify_types_flow import identify_normalization_parameters
  from reagent.workflow.types import Dataset, PreprocessingOptions, ReaderOptions, ResourceOptions, RewardOptions, TableSpec
label_desc: Use image node_id 3 for calling the SyntheticRewardDataModule obj's underlying member method code with example usage: obj.query_data(input_table_spec, sample_range, reward_options, data_fetcher) and returns: data_fetcher