code: def __init__(self, config, prefix, weights):
      super().__init__()
      # self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)
      self.up_proj = TensorParallelColumnLinear.load(
          config,
          prefix=f"{prefix}.up_proj",
          weights=weights,
          bias=not config.no_bias,
      )
      self.act = nn.GELU(approximate="none")
      # self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)
      self.down_proj = TensorParallelRowLinear.load(
          config,
          prefix=f"{prefix}.down_proj",
          weights=weights,
          bias=not config.no_bias,
      )
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from einops import rearrange
  from packaging import version
  from text_generation_server.utils.layers import TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelHead, get_linear
label_desc: Use image node_id 1 to create a new MPTMLP object from inherited base classes: nn with example: obj = MPTMLP(config, prefix, weights)