code: def run(self):
      if self.logger:
          self.logger.init()
  
      tf.keras.utils.set_random_seed(self.args.random_seed)
  
      try:
          logging.info(f"TARGETS: {self.targets}")
  
          model = None
          if self.args.load_model_path:
              model = self.load_model(self.args.load_model_path)
  
          eval_metrics = None
  
          # If a single-task learning model (if only --predict_data_path is
          # provided, as self.targets will not be available, it checks the
          # --tasks arg to discover if its STL or MTL)
          if (
              len(self.targets) == 1
              and len(list(self.targets.values())[0]) == 1
          ):
              if not model:
                  model = self.build_stl_model()
  
              logging.info(f"MODEL: {model}")
              # Single target = Single-Task Learning
              eval_metrics = self.train_eval_stl(model)
          else:
              if not model:
                  model = self.build_mtl_model()
  
              logging.info(f"MODEL: {model}")
              # Multiple targets = Multi-Task Learning
              eval_metrics = self.train_eval_mtl(model)
  
          if self.args.save_model_path:
              logging.info("Saving the model")
              self.save_model(model, self.args.save_model_path)
  
          logging.info("Script finished successfully")
  
          return eval_metrics
  
      finally:
          if self.logger:
              self.logger.teardown()
  
imports: import json
  import logging
  import os
  from datetime import datetime
  import merlin.models.tf
  import numpy
  import tensorflow
  from merlin.io.dataset import Dataset
  from merlin.models.tf.logging.callbacks import ExamplesPerSecondCallback, WandbLogger
  from merlin.models.tf.transforms.negative_sampling import InBatchNegatives
  from merlin.schema.tags import Tags
  from .args_parsing import Task, parse_arguments
  from .mtl import get_mtl_loss_weights, get_mtl_prediction_tasks
  from .ranking_models import get_model
label_desc: Use image node_id 15 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.run() and returns: eval_metrics