code: def __init__(
      self, intermediate_size, config
  ):  # in MLP: intermediate_size= 4 * embed_dim
      super().__init__()
      embed_dim = config.n_embd
  
      self.fc_in = nn.Linear(embed_dim, intermediate_size)
      self.fc_out = nn.Linear(intermediate_size, embed_dim)
  
      self.act = ACT2FN[config.activation_function]
      self.dropout = nn.Dropout(config.resid_pdrop)
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.fx
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging
  from ...utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 1 to create a new GPTJMLP object from inherited base classes: nn with example: obj = GPTJMLP(intermediate_size, config)