code: def train_eval_stl(self, model):
      metrics = self.get_metrics()
      model.compile(
          self.get_optimizer(),
          run_eagerly=False,
          metrics=metrics,
      )
  
      callbacks = self.get_callbacks(self.args)
      class_weights = {0: 1.0, 1: self.args.stl_positive_class_weight}
  
      eval_metrics = None
  
      if self.train_loader:
          logging.info("Starting to train the model")
  
          fit_kwargs = {}
          # if self.eval_loader:
          #     fit_kwargs = {
          #         "validation_data": self.eval_loader,
          #         "validation_steps": self.args.validation_steps,
          #     }
  
          model.fit(
              self.train_loader,
              epochs=self.args.epochs,
              batch_size=self.args.train_batch_size,
              steps_per_epoch=self.args.train_steps_per_epoch,
              drop_last=True,
              callbacks=callbacks,
              train_metrics_steps=self.args.train_metrics_steps,
              class_weight=class_weights,
              **fit_kwargs,
          )
  
      if self.eval_loader:
          logging.info("Starting the evaluation of the model")
  
          eval_metrics = model.evaluate(
              self.eval_loader,
              batch_size=self.args.eval_batch_size,
              return_dict=True,
              callbacks=callbacks,
          )
  
          logging.info(f"EVALUATION METRICS: {eval_metrics}")
          self.log_final_metrics(eval_metrics)
  
      if self.predict_loader:
          self.save_predictions(model, self.predict_loader.dataset)
  
      return eval_metrics
  
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 10 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.train_eval_stl(model) and returns: eval_metrics