code: def __init__(self, config, prefix, weights):
      super().__init__()
      self.num_attention_heads = config.num_attention_heads
      self.hidden_size = config.hidden_size
      self.head_size = self.hidden_size // self.num_attention_heads
      self.rotary_ndims = int(self.head_size * config.rotary_pct)
      max_positions = config.max_position_embeddings
      # ??? TODO
      # self.register_buffer(
      #     "bias",
      #     torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
      #         1, 1, max_positions, max_positions
      #     ),
      # )
      # self.register_buffer("masked_bias", torch.tensor(-1e9))
      self.rotary_emb = RotaryEmbedding(
          self.rotary_ndims,
          config.max_position_embeddings,
          base=config.rotary_emb_base,
      )
      self.rotary_emb.inv_freq = nn.Parameter(
          weights.get_tensor(f"{prefix}.rotary_emb.inv_freq")
      )
      self.inv_norm_factor = 1.0 / torch.sqrt(
          torch.tensor(self.head_size, dtype=torch.float32)
      ).to(torch.get_default_dtype())
  
      if self.num_attention_heads % weights.process_group.size() != 0:
          raise ValueError(
              f"`num_attention_heads` must be divisible by `num_shards` "
              f"(got `num_attention_heads`: {self.num_attention_heads} "
              f"and `num_shards`: {weights.process_group.size()}"
          )
      self.num_attention_heads = (
          self.num_attention_heads // weights.process_group.size()
      )
      self.query_key_value = TensorParallelColumnLinear.load(
          config,
          prefix=f"{prefix}.query_key_value",
          weights=weights,
          bias=True,
      )
      self.dense = TensorParallelRowLinear.load(
          config, prefix=f"{prefix}.dense", weights=weights, bias=True
      )
  
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 1 to create a new GPTNeoXAttention object from inherited base classes: nn with example: obj = GPTNeoXAttention(config, prefix, weights)