Adding Support for Torchtext iterators
I recently came across pytorch lightning and i am absolutely loving it  until now. Not having to worry about my training cycle and making it super efficient and fast. It has increased the amount of experiments i can pull off and good results have come out from it.
Right now, i have been using torchtext with its dataset classes and its custom iterators. But when i tried to use the iterators option from torchtext such as Iterator or BucketIterator instead of Dataloader i get the following error:
 TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not NoneType
The problem is that instead of getting a Tensor im getting a NoneType. And i dont know why that is.
Now, i tried to load the Dataset classes from torchtext with the DataLoader itself and i find the next error:
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'torchtext.data.example.Example'> 
So, ideally i would really like to have the torchtext iterators supported with pytorch-lighting. But i dont know if there is a way around this issue that i havent found, still using the torchtext Dataset classes. Could anybody help me out with this?