code: def import_dataset(
      task_type="binary", split=True, larger=False, seed=0
  ):
      if task_type == "binary":
          df = h2o.import_file(
              path=pu.locate(
                  "smalldata/prostate/{}".format(
                      "prostate_complete.csv.zip"
                      if larger
                      else "prostate.csv"
                  )
              )
          )
          target = "CAPSULE"
          df[target] = df[target].asfactor()
      elif task_type == "multiclass":
          df = h2o.import_file(
              path=pu.locate("smalldata/iris/iris_wheader.csv")
          )
          target = "class"
          df[target] = df[target].asfactor()
      else:  # regression
          df = h2o.import_file(
              path=pu.locate("smalldata/extdata/australia.csv")
          )
          target = "runoffnew"
  
      splits = (
          df.split_frame(
              ratios=[0.8, 0.1],
              destination_frames=[
                  df.key + "_" + f
                  for f in ["training", "validation", "test"]
              ],
              seed=seed,
          )
          if split
          else [df, None, None]
      )
  
      return Namespace(
          train=splits[0],
          valid=splits[1],
          test=splits[2],
          target=target,
          target_idx=1,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: import_dataset(task_type, split, larger, seed) and returns: Namespace