class_docstr: Dataset wrapping tensors.
  
  Each sample will be retrieved by indexing tensors along the first dimension.
  
  Arguments:
      *tensors (Tensor): tensors that have the same size of the first dimension.
class_docstr_tok: ["Dataset","wrapping","tensors",".","Each","sample","will","be","retrieved","by","indexing","tensors","along","the","first","dimension",".","Arguments",":","*","tensors","(","Tensor",")",":","tensors","that","have","the","same","size","of","the","first","dimension","."]
code: def __len__(self):
      return self.tensors[0].size(0)
  
imports: import torch.utils.data
  from torch.utils.data import Dataset
  from torchvision import transforms
  from PIL import Image
  import os
  import os.path
  import sys
label_desc: Use image node_id 3 for calling the TensorDataset obj's underlying member method code with example usage: obj.__len__() and returns: self