class_docstr: Memory Augmented Time Series Dataset
  
  Args:
      handler (DataHandler): data handler
      segments (dict): data split segments
      seq_len (int): time series sequence length
      horizon (int): label horizon (to mask historical loss for TRA)
      num_states (int): how many memory states to be added (for TRA)
      batch_size (int): batch size (<0 means daily batch)
      shuffle (bool): whether shuffle data
      pin_memory (bool): whether pin data to gpu memory
      drop_last (bool): whether drop last batch < batch_size
class_docstr_tok: ["Memory","Augmented","Time","Series","Dataset","Args",":","handler","(","DataHandler",")",":","data","handler","segments","(","dict",")",":","data","split","segments","seq_len","(","int",")",":","time","series","sequence","length","horizon","(","int",")",":","label","horizon","(","to","mask","historical","loss","for","TRA",")","num_states","(","int",")",":","how","many","memory","states","to","be","added","(","for","TRA",")","batch_size","(","int",")",":","batch","size","(","<","0","means","daily","batch",")","shuffle","(","bool",")",":","whether","shuffle","data","pin_memory","(","bool",")",":","whether","pin","data","to","gpu","memory","drop_last","(","bool",")",":","whether","drop","last","batch","<","batch_size"]
code: def _get_slices(self):
      if self.batch_size < 0:
          slices = self.daily_slices.copy()
          batch_size = -1 * self.batch_size
      else:
          slices = self.batch_slices.copy()
          batch_size = self.batch_size
      return slices, batch_size
  
imports: import copy
  import torch
  import numpy
  import pandas
  from qlib.data.dataset import DatasetH
label_desc: Use image node_id 9 for calling the MTSDatasetH obj's underlying member method code with example usage: obj._get_slices() and returns: slices,  batch_size