code: def greedy_search(
      self,
      input_ids,
      encoder_output,
      attention_mask,
      max_length,
      pad_token_id: int,
      eos_token_id: int,
  ):
      # init sequence length tensors
      (
          sequence_lengths,
          unfinished_sequences,
          cur_len,
      ) = self._init_sequence_length_for_generation(
          input_ids, max_length
      )
  
      past: List[torch.Tensor] = []
      while cur_len < max_length:
          logits, past = self._decoder_forward(
              input_ids, encoder_output, attention_mask, past
          )
          next_token_logits = logits[:, -1, :]
  
          # pre-process distribution
          scores = self.logits_processor(input_ids, next_token_logits)
  
          # argmax
          next_tokens = torch.argmax(scores, dim=-1)
  
          # add code that transfomers next_tokens to tokens_to_add
          if eos_token_id is not None:
              assert (
                  pad_token_id is not None
              ), "If eos_token_id is defined, make sure that pad_token_id is defined."
              next_tokens = next_tokens * unfinished_sequences + (
                  pad_token_id
              ) * (1 - unfinished_sequences)
  
          # add token and increase length by one
          input_ids = torch.cat(
              [input_ids, next_tokens[:, None]], dim=-1
          )
  
          # update sequence length
          if eos_token_id is not None:
              (
                  sequence_lengths,
                  unfinished_sequences,
              ) = self._update_seq_length_for_generation(
                  sequence_lengths,
                  unfinished_sequences,
                  cur_len,
                  next_tokens == eos_token_id,
              )
  
          # stop when there is a </s> in each sentence, or if we exceed the maximul length
          if unfinished_sequences.max() == 0:
              break
  
          # increase cur_len
          cur_len = cur_len + 1
  
      return input_ids
  
imports: import copy
  import itertools
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from transformers import BartConfig
  from transformers.generation_utils import GenerationMixin
label_desc: Use image node_id 6 for calling the BARTGenerator obj's underlying member method code with example usage: obj.greedy_search(input_ids, encoder_output, attention_mask, max_length, pad_token_id, eos_token_id) and returns: input_ids