code: def __init__(self, intermediate_size, config):
      super().__init__()
      embed_dim = config.hidden_size
      self.c_fc = Conv1D(intermediate_size, embed_dim)
      self.c_proj = Conv1D(embed_dim, intermediate_size)
      self.act = ACT2FN[config.activation_function]
      self.dropout = nn.Dropout(config.resid_pdrop)
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 to create a new GPT2MLP object from inherited base classes: Module with example: obj = GPT2MLP(intermediate_size, config)