code: def __init__(self, layer_id, config, weights):
      super().__init__()
      prefix = f"transformer.h.{layer_id}"
      self.ln_1 = FastLayerNorm.load(
          prefix=f"{prefix}.ln_1",
          weights=weights,
          eps=config.layer_norm_epsilon,
      )
      self.ln_2 = FastLayerNorm.load(
          prefix=f"{prefix}.ln_2",
          weights=weights,
          eps=config.layer_norm_epsilon,
      )
      self.attn = FlashMQAttention(
          prefix=f"{prefix}.attn",
          config=config,
          weights=weights,
      )
      self.mlp = MLP(
          prefix=f"{prefix}.mlp",
          config=config,
          weights=weights,
      )
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  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, TensorParallelHead, TensorParallelEmbedding, FastLayerNorm, get_linear
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(layer_id, config, weights)