code: def forward(
      self,
      hidden_states: torch.FloatTensor,
      attention_mask: torch.FloatTensor,
      position_ids: torch.LongTensor,
      head_mask: Optional[torch.FloatTensor] = None,
      layer_past: Optional[Tuple[torch.Tensor]] = None,
      use_cache: Optional[bool] = False,
      output_attentions: Optional[bool] = False,
  ):
      has_layer_past = layer_past is not None
  
      # Compute QKV
      # Attention heads [batch, seq_len, hidden_size]
      #   --> [batch, seq_len, (np * 3 * head_size)]
      qkv = self.query_key_value(hidden_states)
  
      # [batch, seq_len, (num_heads * 3 * head_size)]
      #   --> [batch, seq_len, num_heads, 3 * head_size]
      new_qkv_shape = qkv.size()[:-1] + (
          self.num_attention_heads,
          3 * self.head_size,
      )
      qkv = qkv.view(*new_qkv_shape)
  
      # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
      query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
      key = qkv[..., self.head_size : 2 * self.head_size].permute(
          0, 2, 1, 3
      )
      value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
  
      # Compute rotary embeddings on rotary_ndims
      query_rot = query[..., : self.rotary_ndims]
      query_pass = query[..., self.rotary_ndims :]
      key_rot = key[..., : self.rotary_ndims]
      key_pass = key[..., self.rotary_ndims :]
  
      # Compute token offset for rotary embeddings (when decoding)
      seq_len = key.shape[-2]
      if has_layer_past:
          seq_len += layer_past[0].shape[-2]
      cos, sin = self.rotary_emb(value, seq_len=seq_len)
      query, key = apply_rotary_pos_emb(
          query_rot, key_rot, cos, sin, position_ids
      )
      query = torch.cat((query, query_pass), dim=-1)
      key = torch.cat((key, key_pass), dim=-1)
  
      # Cache QKV values
      if has_layer_past:
          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)
      present = (key, value) if use_cache else None
  
      # Compute attention
      attn_output, attn_weights = self._attn(
          query, key, value, attention_mask, head_mask
      )
  
      # Reshape outputs
      attn_output = self._merge_heads(
          attn_output, self.num_attention_heads, self.head_size
      )
      attn_output = self.dense(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
imports: import os
  import pdb
  import copy
  import math
  import numpy
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  import torch.utils.checkpoint
  import torch.nn.functional
  from torch.cuda.amp import autocast
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.models.gpt_neox.modeling_gpt_neox import RotaryEmbedding, GPTNeoXAttention, apply_rotary_pos_emb
label_desc: Use image node_id 3 for calling the GPTNeoXAttention_Mask obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, position_ids, head_mask, layer_past, use_cache, output_attentions) and returns: outputs