code: def __init__(self, config, weights):
      super().__init__()
  
      self.embed_tokens = TensorParallelEmbedding(
          prefix="model.embed_tokens", weights=weights
      )
  
      self.layers = nn.ModuleList(
          [
              MixtralLayer(
                  layer_id,
                  config,
                  weights,
              )
              for layer_id in range(config.num_hidden_layers)
          ]
      )
      self.norm = FastRMSNorm.load(
          prefix="model.norm", weights=weights, eps=config.rms_norm_eps
      )
  
      self.head_size = self.layers[0].self_attn.head_size
      self.num_heads = self.layers[0].self_attn.num_heads
      self.num_key_value_heads = self.layers[
          0
      ].self_attn.num_key_value_heads
  
imports: import torch
  import torch.distributed
  import numpy
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.configuration_utils import PretrainedConfig
  from typing import Optional, List, Tuple
  from loguru import logger
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.layers import FastLinear, FastRMSNorm, TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, PositionRotaryEmbedding, TensorParallelHead, get_linear
label_desc: Use image node_id 1 to create a new MixtralModel object from inherited base classes: torch.nn with example: obj = MixtralModel(config, weights)