code: def fetch_ml_uci(dataset):
      """Get a datasest from ML UCI database.
  
      Parameters
      ----------
      dataset: string
          Dataset name. Must be in NAMES.keys()
  
      Returns
      -------
      X: np.array, shape (n_samples, n_features)
          Design matrix.
      y: np.array, shape (n_samples)
          Target vector.
      """
      if not os.path.exists(pjoin(CELER_PATH, "ml_uci")):
          os.makedirs(pjoin(CELER_PATH, "ml_uci"))
  
      if dataset not in NAMES:
          raise ValueError("Unsupported dataset %s" % dataset)
  
      X_path = pjoin(CELER_PATH, "ml_uci", dataset + "_data.npz")
      y_path = pjoin(CELER_PATH, "ml_uci", dataset + "_target.npy")
      try:
          X = sparse.load_npz(X_path)
          y = np.load(y_path)
      except FileNotFoundError:
          df = pd.read_csv(
              BASE + NAMES[dataset] + ".data", sep=" ", header=None
          )
          # trailing wspace > extra column
          X = sparse.csc_matrix(df.values[:, :-1])
          y = np.array(
              pd.read_csv(
                  BASE + NAMES[dataset] + ".labels", header=None
              )
          ).ravel()
          sparse.save_npz(X_path, X)
          np.save(y_path, y)
  
      return X, y
  
imports: import os
  import numpy
  import pandas
  from scipy import sparse
  from os.path import join
  from celer.datasets import CELER_PATH
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_ml_uci(dataset) and returns: X,  y