class_docstr: Samples elements sequentially, always in the same order.
  
  Args:
      data_source (Dataset): dataset to sample from
class_docstr_tok: ["Samples","elements","sequentially",",","always","in","the","same","order",".","Args",":","data_source","(","Dataset",")",":","dataset","to","sample","from"]
code: def __init__(self, data_source: Sized) -> None:
      self.data_source = data_source
  
imports: import torch
  from torch import Tensor
  from typing import Iterator, Iterable, Optional, Sequence, List, TypeVar, Generic, Sized, Union
label_desc: Use image node_id 1 to create a new SequentialSampler object from inherited base classes: Sampler with example: obj = SequentialSampler(data_source)