class_docstr: A custom iterator for loading files in batches.
code: def __init__(self, file_paths: List[Tuple[str, str]]):
      self._file_paths = file_paths
      self._it = 0
      # XGBoost will generate some cache files under current directory with the prefix
      # "cache"
      super().__init__(cache_prefix=os.path.join(".", "cache"))
  
file_path: xgboost/demo/guide-python/external_memory.py
imports: import os
  import tempfile
  from typing import Callable, List, Tuple
  import numpy
  from sklearn.datasets import make_regression
  import xgboost
label_desc: Use image node_id 1 to create a new Iterator object from inherited base classes: xgboost with example: obj = Iterator(file_paths)