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what issues are you having? torche meta is just a dataloader library, it should be easy (but tedious) to do it since there is nothing that stops torchmeta from working. The model is usually a seperate thing...
I assume that you would like to use an LSTM as a MetaModule, in order to be able to backpropagate through an update (like in MAML)?
Unfortunately at the moment, there is no LSTM MetaModule; however, you could create one by taking the nn.LSTM module in PyTorch, and converting it to a functional module. See the comparison between nn.Linear and MetaLinear for inspiration.
Another option, that would work out of the box with any PyTorch module is to use higher. You can check out this example on how to use Torchmeta and higher together.
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