code: def __init__(self, layer_id, config, weights):
      super().__init__()
  
      layer_norm_eps = config.layer_norm_eps
  
      prefix = f"gpt_neox.layers.{layer_id}"
  
      self.use_parallel_residual = config.use_parallel_residual
      self.input_layernorm = FastLayerNorm.load(
          prefix=f"{prefix}.input_layernorm",
          weights=weights,
          eps=layer_norm_eps,
      )
      self.post_attention_layernorm = FastLayerNorm.load(
          prefix=f"{prefix}.post_attention_layernorm",
          weights=weights,
          eps=layer_norm_eps,
      )
      self.attention = FlashNeoxAttention(
          config, prefix=f"{prefix}.attention", weights=weights
      )
  
      self.mlp = FlashMLP(
          config, prefix=f"{prefix}.mlp", weights=weights
      )
      self.process_group = weights.process_group
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.gpt_neox import GPTNeoXConfig
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.flash_attn import attention
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelHead, FastLayerNorm, PositionRotaryEmbedding, get_linear
label_desc: Use image node_id 1 to create a new FlashNeoXLayer object from inherited base classes: nn with example: obj = FlashNeoXLayer(layer_id, config, weights)