class_docstr: Dataset as a concatenation of multiple TensorDataset datasets with same number of tensors.
  
  Each sample will be retrieved by indexing tensors along the first dimension.
  
  Arguments:
      dataset (TensorDataset): dataset to which rest datasets will be concatinated.
      datasets (List[TensorDataset]): datasets to concat to the dataset.
class_docstr_tok: ["Dataset","as","a","concatenation","of","multiple","TensorDataset","datasets","with","same","number","of","tensors",".","Each","sample","will","be","retrieved","by","indexing","tensors","along","the","first","dimension",".","Arguments",":","dataset","(","TensorDataset",")",":","dataset","to","which","rest","datasets","will","be","concatinated",".","datasets","(","List","[","TensorDataset","]",")",":","datasets","to","concat","to","the","dataset","."]
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 ConcatTensorDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: tuple