code: def forward(
      self,
      hidden_states,
      position_ids,
      attention_mask,
      head_mask=None,
      layer_past=None,
      use_cache=False,
      output_attentions=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).permute(0, 2, 1, 3)
      # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
      query, key, value = qkv.split(self.head_size, -1)
  
      # Compute token offset for rotary embeddings (when decoding)
      seq_len = key.shape[-2]
      if has_layer_past:
          seq_len += layer_past[0].shape[-2]
  
      # Compute rotary embeddings on rotary_ndims
      query_rot = query[..., : self.rotary_ndims]
      key_rot = key[..., : self.rotary_ndims]
  
      query_rot, key_rot = self.rotary_emb(
          query_rot, key_rot, position_ids, seq_len
      )
  
      query[..., : self.rotary_ndims] = query_rot
      key[..., : self.rotary_ndims] = key_rot
  
      if CUSTOM_KERNELS_ENABLED:
          (
              attn_output,
              present,
              attn_weights,
          ) = fused_attention_cuda.forward(
              query,
              key,
              value,
              layer_past,
              attention_mask,
              head_mask,
              self.inv_norm_factor,
              self.num_attention_heads,
              use_cache,
          )
      else:
          # 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: from typing import Optional, Tuple, Union
  import os
  import torch
  import torch.distributed
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers import GPTNeoXConfig
  from loguru import logger
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 2 for calling the GPTNeoXAttention obj's underlying member method code with example usage: obj.forward(hidden_states, position_ids, attention_mask, head_mask, layer_past, use_cache, output_attentions) and returns: outputs