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: Returns the keys that would be returned in `build_serving_modules()`.
  This method is required because we need to reserve entity IDs for
  these serving modules before we start the training.
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code: def serving_module_names(self) -> List[str]:
      """
      Returns the keys that would be returned in `build_serving_modules()`.
      This method is required because we need to reserve entity IDs for
      these serving modules before we start the training.
      """
      return ["default_model"]
  
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 8 for calling the ModelManager obj's underlying member method code with example usage: obj.serving_module_names() and returns: list