code: def infer_forecasting_dataset_tcn(
      X_test, y_test, model, output_path, output_dataset_name="results"
  ):
      y_pred, df_all = model.forecast(X_test, y_test)
  
      run = Run.get_context()
  
      TabularDatasetFactory.register_pandas_dataframe(
          df_all,
          target=(
              run.experiment.workspace.get_default_datastore(),
              datetime.now().strftime("%Y-%m-%d-")
              + str(uuid.uuid4())[:6],
          ),
          name=output_dataset_name,
      )
      df_all.to_csv(
          os.path.join(output_path, output_dataset_name + ".csv"),
          index=False,
      )
  
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 1 for calling a global function with example usage: infer_forecasting_dataset_tcn(X_test, y_test, model, output_path, output_dataset_name) without return types