class_docstr: The MLP block.
  
  Args:
      in_features (int): The input features.
      hidden_features (int, optional): The hidden features.
          Defaults to None.
      out_features (int, optional): The output features.
          Defaults to None.
      drop (float, optional): cfg of dropout function. Defaults to 0.0.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to None.
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code: def __init__(
      self,
      in_features: int,
      hidden_features: int = None,
      out_features: int = None,
      drop: float = 0.0,
      init_cfg: Optional[Union[Dict, List[Dict]]] = None,
  ):
      super().__init__(init_cfg)
      hidden_features = hidden_features or in_features
      out_features = out_features or in_features
      self.fc1 = nn.Linear(in_features, hidden_features)
      self.act = nn.GELU()
      self.fc2 = nn.Linear(hidden_features, out_features)
      self.drop = nn.Dropout(drop)
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmcv.cnn.bricks import DropPath
  from mmengine.model import BaseModule
  from mmengine.model.weight_init import trunc_normal_init
  from mmocr.registry import MODELS
  from mmocr.structures import TextRecogDataSample
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: BaseModule with example: obj = MLP(in_features, hidden_features, out_features, drop, init_cfg)