code: def forward(
      self,
      hidden_states,
      layer_past=None,
      attention_mask=None,
      head_mask=None,
      use_cache=False,
      output_attentions=False,
  ):
      return self.attention(
          hidden_states,
          attention_mask=attention_mask,
          layer_past=layer_past,
          head_mask=head_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
  
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 2 for calling the GPTNeoAttention obj's underlying member method code with example usage: obj.forward(hidden_states, layer_past, attention_mask, head_mask, use_cache, output_attentions) and returns: self