class_docstr: Decoder layer only for inference.
code: def forward(
      self,
      x,
      encoder_out,
      encoder_padding_mask,
      incremental_state,
      self_attn_mask=None,
  ):
      if incremental_state is None:
          cache = None
      else:
          cache = self.get_self_attn_cache(incremental_state)
      self.beam_size = int(x.shape[0] / encoder_padding_mask.shape[0])
      res = super().forward(x, encoder_out, encoder_padding_mask, cache)
      if cache:
          self.set_self_attn_cache(incremental_state, cache)
      return res, None, None
  
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 5 for calling the LSFSTransformerDecoderLayer obj's underlying member method code with example usage: obj.forward(x, encoder_out, encoder_padding_mask, incremental_state, _attn_mask) and returns: res,  None,  None