code: def query_data(
      self,
      input_table_spec: TableSpec,
      sample_range: Optional[Tuple[float, float]],
      reward_options: RewardOptions,
      data_fetcher: DataFetcher,
  ) -> Dataset:
      raise NotImplementedError
  
imports: import logging
  from dataclasses import replace
  from typing import Dict, List, Optional, Tuple
  import reagent.core.types
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.parameters import EvaluationParameters, NormalizationData, NormalizationKey
  from reagent.data.data_fetcher import DataFetcher
  from reagent.data.manual_data_module import ManualDataModule
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
  from reagent.gym.policies.scorers.discrete_scorer import parametric_dqn_scorer
  from reagent.model_managers.model_manager import ModelManager
  from reagent.models.base import ModelBase
  from reagent.preprocessing.batch_preprocessor import BatchPreprocessor
  from reagent.preprocessing.normalization import get_feature_config
  from reagent.preprocessing.types import InputColumn
  from reagent.training import ReAgentLightningModule
  from reagent.workflow.identify_types_flow import identify_normalization_parameters
  from reagent.workflow.types import Dataset, PreprocessingOptions, ReaderOptions, RewardOptions, TableSpec
label_desc: Use image node_id 3 for calling the ParametricDqnDataModule obj's underlying member method code with example usage: obj.query_data(input_table_spec, sample_range, reward_options, data_fetcher) without return types