code: def forward(
      self,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.Tensor] = None,
      decoder_input_ids: Optional[torch.LongTensor] = None,
      decoder_attention_mask: Optional[torch.LongTensor] = None,
      head_mask: Optional[torch.Tensor] = None,
      decoder_head_mask: Optional[torch.Tensor] = None,
      cross_attn_head_mask: Optional[torch.Tensor] = None,
      encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if encoder_outputs is None:
          encoder_outputs = self.encoder(
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
      # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
      elif return_dict and not isinstance(
          encoder_outputs, BaseModelOutput
      ):
          encoder_outputs = BaseModelOutput(
              last_hidden_state=encoder_outputs[0],
              hidden_states=encoder_outputs[1]
              if len(encoder_outputs) > 1
              else None,
              attentions=encoder_outputs[2]
              if len(encoder_outputs) > 2
              else None,
          )
  
      # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
      decoder_outputs = self.decoder(
          input_ids=decoder_input_ids,
          attention_mask=decoder_attention_mask,
          encoder_hidden_states=encoder_outputs[0],
          encoder_attention_mask=attention_mask,
          head_mask=decoder_head_mask,
          cross_attn_head_mask=cross_attn_head_mask,
          past_key_values=past_key_values,
          inputs_embeds=decoder_inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      if not return_dict:
          return decoder_outputs + encoder_outputs
  
      return Seq2SeqModelOutput(
          last_hidden_state=decoder_outputs.last_hidden_state,
          past_key_values=decoder_outputs.past_key_values,
          decoder_hidden_states=decoder_outputs.hidden_states,
          decoder_attentions=decoder_outputs.attentions,
          cross_attentions=decoder_outputs.cross_attentions,
          encoder_last_hidden_state=encoder_outputs.last_hidden_state,
          encoder_hidden_states=encoder_outputs.hidden_states,
          encoder_attentions=encoder_outputs.attentions,
      )
  
imports: import math
  import random
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...deepspeed import is_deepspeed_zero3_enabled
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_m2m_100 import M2M100Config
label_desc: Use image node_id 6 for calling the M2M100Model obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict) and returns: Seq2SeqModelOutput, unknown