code: def load_credit_data():
      sk_data_dir = get_data_home()
      archive = RemoteFileMetadata(
          filename="default of credit card clients.xls",
          url="https://archive.ics.uci.edu/ml/machine-learning-databases/"
          "00350/default%20of%20credit%20card%20clients.xls",
          checksum=(
              "30c6be3abd8dcfd3e6096c828bad8c2f"
              "011238620f5369220bd60cfc82700933"
          ),
      )
  
      if not exists(join(sk_data_dir, archive.filename)):
          _fetch_remote(archive, dirname=sk_data_dir)
  
      data = pd.read_excel(
          join(sk_data_dir, archive.filename),
          sheet_name="Data",
          header=1,
      )
  
      dataset = Bunch(
          data=(data.drop("default payment next month", axis=1)),
          target=np.array(data["default payment next month"]),
      )
      return dataset
  
imports: import pandas
  import numpy
  from sklearn.datasets.base import get_data_home, Bunch
  from sklearn.datasets.base import _fetch_remote, RemoteFileMetadata
  from os.path import exists, join
label_desc: Use image node_id 1 for calling a global function with example usage: load_credit_data() and returns: dataset