class_docstr: 随机顺序的 Sampler 。
  
  :param dataset: 实现了 __len__ 方法的数据容器
  :param shuffle: 是否在每次 iterate 的时候打乱顺序
  :param seed: 随机数种子
  :param kwargs: fastNLP 内部使用的参数
class_docstr_tok: ["\u968f\u673a\u987a\u5e8f\u7684","Sampler","\u3002",":","param","dataset",":","\u5b9e\u73b0\u4e86","__len__","\u65b9\u6cd5\u7684\u6570\u636e\u5bb9\u5668",":","param","shuffle",":","\u662f\u5426\u5728\u6bcf\u6b21","iterate","\u7684\u65f6\u5019\u6253\u4e71\u987a\u5e8f",":","param","seed",":","\u968f\u673a\u6570\u79cd\u5b50",":","param","kwargs",":","fastNLP","\u5185\u90e8\u4f7f\u7528\u7684\u53c2\u6570"]
docstr: 样本的总数
docstr_tok: ["\u6837\u672c\u7684\u603b\u6570"]
code: def num_samples(self):
      """
      样本的总数
      """
      total_len = getattr(self.dataset, "total_len", None)
      if not isinstance(total_len, int):
          total_len = len(self.dataset)
      return total_len
  
imports: from typing import Dict, List, Union, Sequence
  import math
  import numpy
  from fastNLP.core.log import logger
  from fastNLP.core.dataset import DataSet
label_desc: Use image node_id 11 for calling the RandomSampler obj's underlying member method code with example usage: obj.num_samples() and returns: total_len