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 __len__(self):
      return self._part_len
  
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 4 for calling the ShardedIterator obj's underlying member method code with example usage: obj.__len__() and returns: self