code: def __init__(
      self, n_state, config
  ):  # in MLP: n_state=3072 (4 * n_embd)
      super().__init__()
      nx = config.n_embd
      self.c_fc = Conv1D(n_state, nx)
      self.c_proj = Conv1D(nx, n_state)
      self.act = ACT2FN[config.activation_function]
      self.dropout = nn.Dropout(config.resid_pdrop)
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: nn with example: obj = MLP(n_state, config)