code: def __init__(
      self, intermediate_size, config
  ):  # in MLP: intermediate_size= 4 * hidden_size
      super().__init__()
      embed_dim = config.hidden_size
      self.c_fc = nn.Linear(embed_dim, intermediate_size)
      self.c_proj = nn.Linear(intermediate_size, embed_dim)
      self.act = ACT2FN[config.activation_function]
      self.dropout = nn.Dropout(float(config.resid_dropout))
  
imports: import os
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 1 to create a new GPTNeoMLP object from inherited base classes: nn with example: obj = GPTNeoMLP(intermediate_size, config)