code: def load_model(self, path):
      logging.info(f"Loading model from: {path}")
      model = mm.Model.load(path)
  
      # Loading the model targets
      output_targets_path = os.path.join(path, "targets.json")
      logging.info(
          f"Loading model targets metadata from: {output_targets_path}"
      )
      with open(output_targets_path, "r") as outfile:
          self.targets = json.loads(outfile.read())
  
      return model
  
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 17 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.load_model(path) and returns: model