class_docstr: 按照顺序读取 dataset。
  
  :param dataset: 实现了 __len__ 方法的数据容器。
  :param chunk_dist: 如果为 ``True`` ，当多卡时将不间隔索取数据；为 ``False`` 时则会间隔取数据。假设 dataset 有 10 个 sample ，使用
      2 卡，如果为 ``True`` ，卡 **0** 拿 [0, 1, 2, 3, 4], 卡 **1** 拿 [5, 6, 7, 8, 9] ； 如果为 ``False`` ，则卡 **0** 拿 [0, 2, 4, 8, 8], 
      卡 **1** 拿 [1, 3, 5, 7, 9] 。
  :param kwargs:
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code: def __init__(self, dataset, chunk_dist=False, **kwargs):
      kwargs["shuffle"] = False
      kwargs["seed"] = 0
      super(UnrepeatedSequentialSampler, self).__init__(
          dataset, **kwargs
      )
      self.chunk_dist = chunk_dist
  
imports: from typing import List, Union
  from fastNLP.core.dataset import DataSet
  import numpy
label_desc: Use image node_id 1 to create a new UnrepeatedSequentialSampler object from inherited base classes: UnrepeatedRandomSampler with example: obj = UnrepeatedSequentialSampler(dataset, chunk_dist)