code: def forward(
      self,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
  ):
      transformer_outputs = self.transformer(
          input_ids=input_ids,
          attention_mask=attention_mask,
      )
      lm_logits = self.lm_head(transformer_outputs)
  
      return lm_logits
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from transformers.activations import ACT2FN
  from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
  from transformers.pytorch_utils import Conv1D
label_desc: Use image node_id 2 for calling the GPT2LMHeadModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask) and returns: lm_logits