class_docstr: A sharded wrapper around an iterable (padded to length).
  
  Parameters
  ----------
  sampler
  num_parts
      Number of partitions which the data is split into (default: 1)
  part_index
      The index of the part to read from
  even_size
      If the number of batches is not even across all partitions, sample a few extra batches
      for the ones with fewer batches.
class_docstr_tok: ["A","sharded","wrapper","around","an","iterable","(","padded","to","length",")",".","Parameters","--","--","--","--","--","sampler","num_parts","Number","of","partitions","which","the","data","is","split","into","(","default",":","1",")","part_index","The","index","of","the","part","to","read","from","even_size","If","the","number","of","batches","is","not","even","across","all","partitions",",","sample","a","few","extra","batches","for","the","ones","with","fewer","batches","."]
code: def __repr__(self):
      ret = (
          "{name}(\n"
          "  total_batch_num={batch_num},\n"
          "  part_batch_num={part_batch_num},\n"
          "  num_parts={num_parts},\n"
          "  part_index={part_index},\n"
          "  rng={rng}\n"
          ")".format(
              name=self.__class__.__name__,
              batch_num=len(self._sampler),
              part_batch_num=self._part_len,
              num_parts=self._num_parts,
              part_index=self._part_index,
              rng=self._rng,
          )
      )
      return ret
  
imports: import math
  import random
  import warnings
  import numpy
  import abc
  import logging
  from typing import Union, Sequence, Optional, List
  from ..base import INT_TYPES
label_desc: Use image node_id 5 for calling the ShardedIterator obj's underlying member method code with example usage: obj.__repr__() and returns: ret