docstr: Selects checkpoints and computes weights for the selected checkpoints.
  
  Parameters
  ----------
  checkpoints
      List of checkpoint information dictionaries.
  Returns
  -------
      List of selected checkpoint paths and list of corresponding
      weights.
docstr_tok: ["Selects","checkpoints","and","computes","weights","for","the","selected","checkpoints",".","Parameters","--","--","--","--","--","checkpoints","List","of","checkpoint","information","dictionaries",".","Returns","--","--","--","-","List","of","selected","checkpoint","paths","and","list","of","corresponding","weights","."]
code: def select_checkpoints(
      self, checkpoints: List[Dict]
  ) -> Tuple[List[str], List[float]]:
      r"""
      Selects checkpoints and computes weights for the selected checkpoints.
  
      Parameters
      ----------
      checkpoints
          List of checkpoint information dictionaries.
      Returns
      -------
          List of selected checkpoint paths and list of corresponding
          weights.
      """
      raise NotImplementedError()
  
imports: import glob
  import json
  import logging
  from typing import Dict, List, Tuple
  import mxnet
  import mxnet.gluon.nn
  import numpy
  from gluonts.core.component import validated
  from .callback import Callback
label_desc: Use image node_id 4 for calling the AveragingStrategy obj's underlying member method code with example usage: obj.select_checkpoints(checkpoints) without return types