code: def get_model_filename(run, model_name, model_path):
      model = Model(run.experiment.workspace, model_name)
      if "model_file_name" in model.tags:
          return model.tags["model_file_name"]
      is_pkl = True
      if model.tags.get(
          "algorithm"
      ) == "TCNForecaster" or os.path.exists(
          os.path.join(model_path, "model.pt")
      ):
          is_pkl = False
      return "model.pkl" if is_pkl else "model.pt"
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
imports: import argparse
  from datetime import datetime
  import os
  import uuid
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  import joblib
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from azureml.data.dataset_factory import TabularDatasetFactory
  from azureml.automl.runtime.shared.score import scoring, constants
  import azureml.automl.core.shared.constants
  from azureml.core import Run, Dataset, Model
label_desc: Use image node_id 6 for calling a global function with example usage: get_model_filename(run, model_name, model_path) and returns: unknown, model