code: def __init__(self, config, prefix, weights):
      super().__init__()
      self.act = (
          ACT2FN[config.hidden_act]
          if "gelu_fast" not in config.hidden_act
          else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
      )
  
      self.dense_h_to_4h = TensorParallelColumnLinear.load(
          config,
          prefix=f"{prefix}.dense_h_to_4h",
          weights=weights,
          bias=True,
      )
      self.dense_4h_to_h = TensorParallelRowLinear.load(
          config,
          prefix=f"{prefix}.dense_4h_to_h",
          weights=weights,
          bias=True,
      )
  
imports: from typing import Optional, Tuple, Union
  import os
  import torch
  import torch.distributed
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers import GPTNeoXConfig
  from loguru import logger
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 1 to create a new GPTNeoXMLP object from inherited base classes: nn with example: obj = GPTNeoXMLP(config, prefix, weights)