All RNN modules accept packed sequences as inputs.
Note
Instances of this class should never be created manually. They are meant to be instantiated by functions like pack_padded_sequence().
Batch sizes represent the number elements at each sequence step in the batch, not the varying sequence lengths passed to pack_padded_sequence(). For instance, given data abc and x the PackedSequence would contain data axbc with batch_sizes=[2,1,1].
Note
data can be on arbitrary device and of arbitrary dtype. sorted_indices and unsorted_indices must be torch.int64 tensors on the same device as data.
However, batch_sizes should always be a CPU torch.int64 tensor.
This invariant is maintained throughout PackedSequence class, and all functions that construct a PackedSequence in PyTorch (i.e., they only pass in tensors conforming to this constraint).
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