code: def __init__(
      self, config, in_features: int, hidden_features: int
  ) -> None:
      super().__init__()
      self.fc1 = nn.Linear(in_features, hidden_features)
      self.act = ACT2FN[config.hidden_act]
      self.fc2 = nn.Linear(hidden_features, in_features)
      self.drop = nn.Dropout(config.dropout_prob)
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_vitdet import VitDetConfig
label_desc: Use image node_id 1 to create a new VitDetMlp object from inherited base classes: nn with example: obj = VitDetMlp(config, in_features, hidden_features)