code: def retrieve_dataset(dataset, **kwargs):
      # if data not extracted, download zip and extract
      outdirname = "datasets.1.17.2019"
      if not os.path.exists(outdirname):
          try:
              from urllib import urlretrieve
          except ImportError:
              from urllib.request import urlretrieve
          import zipfile
  
          zipfilename = outdirname + ".zip"
          urlretrieve(
              "https://publictestdatasets.blob.core.windows.net/data/"
              + zipfilename,
              zipfilename,
          )
          with zipfile.ZipFile(zipfilename, "r") as unzip:
              unzip.extractall(".")
      extension = os.path.splitext(dataset)[1]
      filepath = os.path.join(outdirname, dataset)
      if extension == ".npz":
          # sparse format file
          import scipy.sparse as sparse
  
          return sparse.load_npz(filepath)
      elif extension == ".svmlight":
          from sklearn import datasets
  
          return datasets.load_svmlight_file(filepath)
      elif extension == ".json":
          import json
  
          with open(filepath, encoding="utf-8") as f:
              dataset = json.load(f)
          return dataset
      elif extension == ".csv":
          import pandas as pd
  
          return pd.read_csv(filepath, **kwargs)
      else:
          raise Exception("Unrecognized file extension: " + extension)
  
label_desc: Use image node_id 1 for calling a global function with example usage: retrieve_dataset(dataset) and returns: sparse, datasets, dataset, pd