code: def forward(
      self,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
  ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
      input_shape = input_ids.size()
      input_ids = input_ids.view(-1, input_shape[-1])
      batch_size = input_ids.shape[0]
  
      device = input_ids.device
  
      past_length = 0
      past_key_values = tuple([None] * len(self.h))
  
      position_ids = torch.arange(
          past_length,
          input_shape[-1] + past_length,
          dtype=torch.long,
          device=device,
      )
      position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
  
      # GPT2Attention mask.
      attention_mask = attention_mask.view(batch_size, -1)
      attention_mask = attention_mask[:, None, None, :]
      attention_mask = attention_mask.to(
          dtype=self.dtype
      )  # fp16 compatibility
      attention_mask = (1.0 - attention_mask) * -10000.0
  
      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # head_mask has shape n_layer x batch x n_heads x N x N
      head_mask = self.get_head_mask(head_mask, self.config.n_layer)
      inputs_embeds = self.wte(input_ids)
      position_embeds = self.wpe(position_ids)
  
      hidden_states = inputs_embeds + position_embeds
  
      output_shape = input_shape + (hidden_states.size(-1),)
  
      for i, (block, layer_past) in enumerate(
          zip(self.h, past_key_values)
      ):
          outputs = block(
              hidden_states,
              attention_mask=attention_mask,
              head_mask=head_mask[i],
          )
          hidden_states = outputs
  
      hidden_states = self.ln_f(hidden_states)
      hidden_states = hidden_states.view(output_shape)
  
      return hidden_states
  
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 GPT2Model obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask) and returns: hidden_states