code: def run_publisher(
      publisher: ModelPublisher__Union,
      model_chooser: ModelManager__Union,
      training_output: RLTrainingOutput,
      setup_data: Optional[Dict[str, bytes]],
      recurring_workflow_ids: ModuleNameToEntityId,
      child_workflow_id: int,
      recurring_period: Optional[RecurringPeriod],
  ) -> RLTrainingOutput:
      assert (
          training_output.publishing_result is None
      ), f"publishing_output was set to f{training_output.publishing_output}"
      model_publisher = publisher.value
      model_manager = model_chooser.value
      publishing_result = model_publisher.publish(
          model_manager,
          training_output,
          setup_data,
          recurring_workflow_ids,
          child_workflow_id,
          recurring_period,
      )
      return dataclasses.replace(
          training_output, publishing_result=publishing_result
      )
  
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 5 for calling a global function with example usage: run_publisher(publisher, model_chooser, training_output, setup_data, recurring_workflow_ids, child_workflow_id, recurring_period) and returns: dataclasses