class_docstr: ModelManager manages how to train models.
  
  Each type of models can have their own config type, implemented as
  `config_type()` class method. `__init__()` of the concrete class must take
  this type.
  
  To integrate training algorithms into the standard training workflow, you need:
  1. `build_trainer()`: Builds the ReAgentLightningModule
  2. `get_data_module()`: Defines how to create data module for this algorithm
  3. `build_serving_modules()`: Creates the TorchScript modules for serving
  4. `get_reporter()`: Returns the reporter to collect training/evaluation metrics
  5. `create_policy()`: (Optional) Creates Policy object for to interact with Gym
docstr: Return the data module. If this is not None, then `run_feature_identification` &
  `query_data` will not be run.
docstr_tok: ["Return","the","data","module",".","If","this","is","not","None",",","then","`","run_feature_identification","`","&","`","query_data","`","will","not","be","run","."]
code: def get_data_module(
      self,
      *,
      input_table_spec: Optional[TableSpec] = None,
      reward_options: Optional[RewardOptions] = None,
      setup_data: Optional[Dict[str, bytes]] = None,
      saved_setup_data: Optional[Dict[str, bytes]] = None,
      reader_options: Optional[ReaderOptions] = None,
      resource_options: Optional[ResourceOptions] = None,
  ) -> Optional[ReAgentDataModule]:
      """
      Return the data module. If this is not None, then `run_feature_identification` &
      `query_data` will not be run.
      """
      return None
  
imports: import abc
  import logging
  from typing import Dict, List, Optional, Tuple
  import pytorch_lightning
  import torch
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import NormalizationData
  from reagent.data.reagent_data_module import ReAgentDataModule
  from reagent.reporting.reporter_base import ReporterBase
  from reagent.training import MultiStageTrainer, ReAgentLightningModule
  from reagent.workflow.types import Dataset, ReaderOptions, ResourceOptions, RewardOptions, RLTrainingOutput, RLTrainingReport, TableSpec
  from reagent.workflow.utils import get_rank, train_eval_lightning
label_desc: Use image node_id 2 for calling the ModelManager obj's underlying member method code with example usage: obj.get_data_module() and returns: None