class_docstr: Base class for the extra block in the FPN.
  
  Args:
      results (List[Tensor]): the result of the FPN
      x (List[Tensor]): the original feature maps
      names (List[str]): the names for each one of the
          original feature maps
  
  Returns:
      results (List[Tensor]): the extended set of results
          of the FPN
      names (List[str]): the extended set of names for the results
class_docstr_tok: ["Base","class","for","the","extra","block","in","the","FPN",".","Args",":","results","(","List","[","Tensor","]",")",":","the","result","of","the","FPN","x","(","List","[","Tensor","]",")",":","the","original","feature","maps","names","(","List","[","str","]",")",":","the","names","for","each","one","of","the","original","feature","maps","Returns",":","results","(","List","[","Tensor","]",")",":","the","extended","set","of","results","of","the","FPN","names","(","List","[","str","]",")",":","the","extended","set","of","names","for","the","results"]
code: def forward(
      self, results: List[Tensor], x: List[Tensor], names: List[str]
  ) -> Tuple[List[Tensor], List[str]]:
      pass
  
imports: from collections import OrderedDict
  import torch.nn
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.jit.annotations import Tuple, List, Dict
label_desc: Use image node_id 1 for calling the ExtraFPNBlock obj's underlying member method code with example usage: obj.forward(results, x, names) without return types