code: def get_model(schema, prediction_tasks, args):
      if args.model == "mlp":
          model = get_mlp_model(schema, args, prediction_tasks)
      elif args.model == "dcn":
          model = get_dcn_model(schema, args, prediction_tasks)
      elif args.model == "dlrm":
          model = get_dlrm_model(schema, args, prediction_tasks)
      elif args.model == "mlp":
          model = get_mlp_model(schema, args, prediction_tasks)
      elif args.model == "wide_n_deep":
          model = get_wide_and_deep_model(
              schema, args, prediction_tasks
          )
      elif args.model == "deepfm":
          model = get_deepfm_model(schema, args, prediction_tasks)
      # Multi-Task Learning specific models
      elif args.model == "mmoe":
          model = get_mmoe_model(schema, args, prediction_tasks)
      elif args.model == "cgc":
          model = get_cgc_model(schema, args, prediction_tasks)
      elif args.model == "ple":
          model = get_ple_model(schema, args, prediction_tasks)
      else:
          raise ValueError(f"Invalid option for --model: {args.model}")
  
      return model
  
file_path: Merlin/examples/quick_start/scripts/ranking/ranking_models.py
imports: from functools import partial
  import merlin.models.tf
  from merlin.models.tf import Model
  from merlin.models.utils.schema_utils import infer_embedding_dim
  from merlin.schema.tags import Tags
  from tensorflow.keras import regularizers
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(schema, prediction_tasks, args) and returns: model