class_docstr: Dataset as a concatenation of tensor datasets with different number of
  tensors (labeled dataset/ unlabeled dataset). Labels of unlabeled dataset will
  be represented as a tensor of zeros.
  
  Each sample will be retrieved by indexing tensors along the first dimension.
  
  Arguments:
      datasets (List[TensorDataset]): datasets to concat.
class_docstr_tok: ["Dataset","as","a","concatenation","of","tensor","datasets","with","different","number","of","tensors","(","labeled","dataset\/","unlabeled","dataset",")",".","Labels","of","unlabeled","dataset","will","be","represented","as","a","tensor","of","zeros",".","Each","sample","will","be","retrieved","by","indexing","tensors","along","the","first","dimension",".","Arguments",":","datasets","(","List","[","TensorDataset","]",")",":","datasets","to","concat","."]
code: def __getitem__(self, index):
      return tuple(tensor[index] for tensor in self.tensors)
  
file_path: nlp-architect/nlp_architect/nn/torch/data/dataset.py
imports: import torch
  from typing import List
label_desc: Use image node_id 2 for calling the CombinedTensorDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: tuple