code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      layer_past=None,
      head_mask=None,
      use_cache=False,
      output_attentions=False,
  ):
      query = self.q_proj(hidden_states)
      key = self.k_proj(hidden_states)
      value = self.v_proj(hidden_states)
  
      query = self._split_heads(query, self.num_heads, self.head_dim)
      key = self._split_heads(key, self.num_heads, self.head_dim)
      value = self._split_heads(value, self.num_heads, self.head_dim)
  
      if layer_past is not None:
          past_key = layer_past[0]
          past_value = layer_past[1]
          key = torch.cat((past_key, key), dim=-2)
          value = torch.cat((past_value, value), dim=-2)
  
      if use_cache is True:
          present = (key, value)
      else:
          present = None
  
      attn_output, attn_weights = self._attn(
          query, key, value, attention_mask, head_mask
      )
  
      attn_output = self._merge_heads(
          attn_output, self.num_heads, self.head_dim
      )
      attn_output = self.out_proj(attn_output)
      attn_output = self.resid_dropout(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
imports: import os
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 5 for calling the GPTNeoSelfAttention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, layer_past, head_mask, use_cache, output_attentions) and returns: outputs