outputs as being of shape: (batch_size, 2048, 768). Next, one performs cross-attention with the final hidden states
of the latents to update the outputs tensor. After cross-attention, one still has a tensor of shape (batch_size,
2048, 768). One can then place a regular language modeling head on top, to project the last dimension to the
vocabulary size of the model, i.e. creating logits of shape (batch_size, 2048, 262) (as Perceiver uses a vocabulary
size of 262 byte IDs).
 
 Perceiver IO architecture. Taken from the original paper 
This model was contributed by nielsr. The original code can be found
here.
Tips:
The quickest way to get started with the Perceiver is by checking the tutorial
  notebooks.
Refer to the blog post if you want to fully understand how the model works and
is implemented in the library. Note that the models available in the library only showcase some examples of what you can do
