code: def forward(
      self,
      hidden_states,
      attention_mask,
      head_mask=None,
      layer_past=None,
      use_cache=False,
      output_attentions=False,
  ):
      has_layer_past = (
          layer_past is not None and layer_past[0].numel() > 0
      )
  
      # 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]
      offset = 0
      if has_layer_past:
          offset = layer_past[0].shape[-2]
          seq_len += offset
      cos, sin = self.rotary_emb(value, seq_len=seq_len)
      query, key = apply_rotary_pos_emb(
          query_rot, key_rot, cos, sin, offset=offset
      )
      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, self.dense_bias
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_gpt_neox_japanese import GPTNeoXJapaneseConfig
label_desc: Use image node_id 2 for calling the GPTNeoXJapaneseAttention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, layer_past, use_cache, output_attentions) and returns: outputs,  self