code: def __init__(self, layer_id, config, weights):
      super().__init__()
      prefix = f"model.layers.{layer_id}"
      self.self_attn = FlashLlamaAttention(
          prefix=f"{prefix}.self_attn", config=config, weights=weights
      )
      self.mlp = LlamaMLP(
          prefix=f"{prefix}.mlp", config=config, weights=weights
      )
  
      self.input_layernorm = FastRMSNorm.load(
          prefix=f"{prefix}.input_layernorm",
          weights=weights,
          eps=config.rms_norm_eps,
      )
      self.post_attention_layernorm = FastRMSNorm.load(
          prefix=f"{prefix}.post_attention_layernorm",
          weights=weights,
          eps=config.rms_norm_eps,
      )
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.configuration_utils import PretrainedConfig
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, PositionRotaryEmbedding, TensorParallelHead, get_linear, FastRMSNorm
label_desc: Use image node_id 1 to create a new FlashLlamaLayer object from inherited base classes: nn with example: obj = FlashLlamaLayer(layer_id, config, weights)