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.
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code: def __init__(
      self,
      dataset: torch.utils.data.TensorDataset,
      datasets: List[torch.utils.data.TensorDataset],
  ):
      tensors = dataset.tensors
      for ds in datasets:
          concat_tensors = []
          for i, t in enumerate(ds.tensors):
              concat_tensors.append(torch.cat((tensors[i], t), 0))
          tensors = concat_tensors
      assert all(
          tensors[0].size(0) == tensor.size(0) for tensor in tensors
      )
      self.tensors = 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 1 to create a new ConcatTensorDataset object from inherited base classes: torch.utils.data with example: obj = ConcatTensorDataset(dataset, datasets)