code: def get_data(
      run, fitted_model, target_column_name, test_dataset_name
  ):
      # get input dataset by name
      test_dataset = Dataset.get_by_name(
          run.experiment.workspace, test_dataset_name
      )
      test_df = test_dataset.to_pandas_dataframe()
      if target_column_name in test_df:
          y_test = test_df.pop(target_column_name).values
      else:
          y_test = np.full(test_df.shape[0], np.nan)
  
      return test_df, y_test
  
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 5 for calling a global function with example usage: get_data(run, fitted_model, target_column_name, test_dataset_name) and returns: test_df,  y_test