code: def train():
      """
      Train the Merlin model.
      """
      train_path = os.path.join(args.train_dir, "*.parquet")
      valid_path = os.path.join(args.valid_dir, "*.parquet")
  
      workflow = create_nvtabular_workflow(
          train_path=train_path,
          valid_path=valid_path,
      )
  
      train_dataset = nvt.Dataset(train_path)
      valid_dataset = nvt.Dataset(valid_path)
  
      output_path = tempfile.mkdtemp()
      workflow_path = os.path.join(output_path, "workflow")
  
      workflow.fit(train_dataset)
      workflow.transform(train_dataset).to_parquet(
          output_path=os.path.join(output_path, "train")
      )
      workflow.transform(valid_dataset).to_parquet(
          output_path=os.path.join(output_path, "valid")
      )
  
      workflow.save(workflow_path)
      logger.info(f"Workflow saved to {workflow_path}.")
  
      train_data = merlin.io.Dataset(
          os.path.join(output_path, "train", "*.parquet")
      )
      valid_data = merlin.io.Dataset(
          os.path.join(output_path, "valid", "*.parquet")
      )
  
      schema = train_data.schema
      target_column = schema.select_by_tag(Tags.TARGET).column_names[0]
  
      model = mm.DLRMModel(
          schema,
          embedding_dim=64,
          bottom_block=mm.MLPBlock([128, 64]),
          top_block=mm.MLPBlock([128, 64, 32]),
          prediction_tasks=mm.BinaryClassificationTask(target_column),
      )
  
      model.compile(
          "adam", run_eagerly=False, metrics=[tf.keras.metrics.AUC()]
      )
  
      batch_size = args.batch_size
      epochs = args.epochs
      logger.info(f"batch_size = {batch_size}, epochs = {epochs}")
  
      model.fit(
          train_data,
          validation_data=valid_data,
          batch_size=args.batch_size,
          epochs=epochs,
          verbose=2,
      )
  
      model_path = os.path.join(output_path, "dlrm")
      model.save(model_path)
      logger.info(f"Model saved to {model_path}.")
  
      # We remove the label columns from its inputs.
      # This removes all columns with the TARGET tag from the workflow.
      # We do this because we need to set the workflow to only require the
      # features needed to predict, not train, when creating an inference
      # pipeline.
      label_columns = workflow.output_schema.select_by_tag(
          Tags.TARGET
      ).column_names
      workflow.remove_inputs(label_columns)
  
      ensemble = create_ensemble(workflow, model)
      ensemble_path = args.model_dir
      ensemble.export(ensemble_path)
      logger.info(f"Ensemble graph saved to {ensemble_path}.")
  
file_path: Merlin/examples/sagemaker-tensorflow/train.py
imports: import argparse
  import json
  import logging
  import os
  import sys
  import tempfile
  import merlin.io
  import merlin.models.tf
  import nvtabular
  import tensorflow
  from merlin.schema.tags import Tags
  from merlin.systems.dag.ops.workflow import TransformWorkflow
  from merlin.systems.dag.ops.tensorflow import PredictTensorflow
  from merlin.systems.dag.ensemble import Ensemble
  import numpy
  from nvtabular.ops import *
label_desc: Use image node_id 4 for calling a global function with example usage: train() without return types