code: def forward(
      self,
      hidden_states: Optional[Tuple[torch.FloatTensor]],
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
  ) -> Union[
      Tuple[torch.Tensor],
      Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]],
  ]:
      residual = hidden_states
      # %transformer_h_0_ln_1
      hidden_states = self.ln_1(hidden_states)
      attn_outputs = self.attn(
          hidden_states,
          attention_mask=attention_mask,
          head_mask=head_mask,
      )
      # residual connection
      hidden_states = attn_outputs + residual
      residual = hidden_states
      hidden_states = self.ln_2(hidden_states)
      feed_forward_hidden_states = self.mlp(hidden_states)
      # residual connection
      hidden_states = residual + feed_forward_hidden_states
  
      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 GPT2Block obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask) and returns: hidden_states