code: def identify_and_train_network(
      input_table_spec: TableSpec,
      model: ModelManager__Union,
      num_epochs: int,
      use_gpu: Optional[bool] = None,
      reward_options: Optional[RewardOptions] = None,
      reader_options: Optional[ReaderOptions] = None,
      resource_options: Optional[ResourceOptions] = None,
      warmstart_path: Optional[str] = None,
      validator: Optional[ModelValidator__Union] = None,
      publisher: Optional[ModelPublisher__Union] = None,
  ) -> RLTrainingOutput:
      if use_gpu is None:
          # pyre-fixme[35]: Target cannot be annotated.
          use_gpu: bool = torch.cuda.is_available()
  
      reward_options = reward_options or RewardOptions()
      reader_options = reader_options or ReaderOptions()
  
      manager = model.value
  
      normalization_data_map = None
      setup_data = None
  
      data_module = manager.get_data_module(
          input_table_spec=input_table_spec,
          reward_options=reward_options,
          reader_options=reader_options,
          resource_options=resource_options,
      )
      if data_module is not None:
          data_module.prepare_data()
          setup_data = data_module.setup_data
      else:
          normalization_data_map = manager.run_feature_identification(
              input_table_spec
          )
  
      return query_and_train(
          input_table_spec,
          model,
          num_epochs,
          use_gpu=use_gpu,
          setup_data=setup_data,
          normalization_data_map=normalization_data_map,
          reward_options=reward_options,
          reader_options=reader_options,
          resource_options=resource_options,
          warmstart_path=warmstart_path,
          validator=validator,
          publisher=publisher,
      )
  
imports: import dataclasses
  import logging
  import time
  from typing import Dict, Optional
  import torch
  from reagent.core.parameters import NormalizationData
  from reagent.core.tensorboardX import summary_writer_context
  from reagent.data.manual_data_module import get_sample_range
  from reagent.data.oss_data_fetcher import OssDataFetcher
  from reagent.model_managers.model_manager import ModelManager
  from reagent.model_managers.union import ModelManager__Union
  from reagent.publishers.union import ModelPublisher__Union
  from reagent.validators.union import ModelValidator__Union
  from reagent.workflow.env import get_new_named_entity_ids, get_workflow_id
  from reagent.workflow.types import Dataset, ModuleNameToEntityId, ReaderOptions, RecurringPeriod, ResourceOptions, RewardOptions, RLTrainingOutput, TableSpec
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 1 for calling a global function with example usage: identify_and_train_network(input_table_spec, model, num_epochs, use_gpu, reward_options, reader_options, resource_options, warmstart_path, validator, publisher) and returns: query_and_train