code: def initialize_output_tokens(self, encoder_out, src_tokens):
      initial_output_tokens = src_tokens.new_zeros(
          src_tokens.size(0), 2
      )
      initial_output_tokens[:, 0] = self.bos
      initial_output_tokens[:, 1] = self.eos
  
      initial_output_scores = initial_output_tokens.new_zeros(
          *initial_output_tokens.size()
      ).type_as(encoder_out["encoder_out"][0])
  
      return DecoderOut(
          output_tokens=initial_output_tokens,
          output_scores=initial_output_scores,
          attn=None,
          step=0,
          max_step=0,
          history=None,
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.iterative_refinement_generator import DecoderOut
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
  from fairseq.models.transformer import Embedding
  from fairseq.modules import TransformerDecoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from .levenshtein_utils import _apply_del_words, _apply_ins_masks, _apply_ins_words, _fill, _get_del_targets, _get_ins_targets, _skip, _skip_encoder_out
label_desc: Use image node_id 6 for calling the LevenshteinTransformerModel obj's underlying member method code with example usage: obj.initialize_output_tokens(encoder_out, src_tokens) and returns: DecoderOut