code: def __init__(self, config, weights):
      super().__init__()
      self.config = config
  
      self.process_group = weights.process_group
      self.wte = TensorParallelEmbedding(
          prefix="transformer.wte",
          weights=weights,
          reduce=False,
      )
      self.wpe = TensorParallelEmbedding(
          prefix="transformer.wpe",
          weights=weights,
          reduce=False,
      )
  
      self.h = nn.ModuleList(
          [
              Block(
                  layer_id,
                  config,
                  weights,
              )
              for layer_id in range(config.num_hidden_layers)
          ]
      )
      self.ln_f = FastLayerNorm.load(
          prefix="transformer.ln_f",
          weights=weights,
          eps=config.layer_norm_epsilon,
      )
  
      self.head_size = self.h[0].attn.head_size
      self.num_heads = self.h[0].attn.num_heads
  
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 FlashSantacoderModel object from inherited base classes: nn with example: obj = FlashSantacoderModel(config, weights)