class_docstr: Decoder layer only for inference.
code: def get_self_attn_cache(self, incremental_state):
      res = self.get_incremental_state(incremental_state, "cache")
      if res is not None:
          return res
      else:
          return {}
  
file_path: lightseq/lightseq/training/cli/fs_modules/ls_fs_transformer_decoder_layer.py
imports: from fairseq.incremental_decoding_utils import with_incremental_state
  from lightseq.training.ops.pytorch.transformer_decoder_layer import LSTransformerDecoderLayer
label_desc: Use image node_id 2 for calling the LSFSTransformerDecoderLayer obj's underlying member method code with example usage: obj.get_self_attn_cache(incremental_state) and returns: res, dict