code: def __init__(self, layer_id, config, weights):
      super().__init__()
      self.use_parallel_residual = config.use_parallel_residual
      self.input_layernorm = nn.LayerNorm.load(
          prefix=f"gpt_neox.layers.{layer_id}.input_layernorm",
          weights=weights,
          eps=config.layer_norm_eps,
      )
      self.post_attention_layernorm = nn.LayerNorm.load(
          prefix=f"gpt_neox.layers.{layer_id}.post_attention_layernorm",
          weights=weights,
          eps=config.layer_norm_eps,
      )
      self.attention = GPTNeoXAttention(
          config,
          prefix=f"gpt_neox.layers.{layer_id}.attention",
          weights=weights,
      )
      self.mlp = GPTNeoXMLP(
          config,
          prefix=f"gpt_neox.layers.{layer_id}.mlp",
          weights=weights,
      )
  
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 GPTNeoXLayer object from inherited base classes: nn with example: obj = GPTNeoXLayer(layer_id, config, weights)