  the documentation of each model for more information on each specific model's labels.
The base models (e.g., [BertModel]) do not accept labels, as these are the base transformer
models, simply outputting features.
Decoder input IDs
This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
way specific to each model.
Most encoder-decoder models (BART, T5) create their decoder_input_ids on their own from the labels. In
such models, passing the labels is the preferred way to handle training.
Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
Feed Forward Chunking
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
