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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docstr: Forward function.
  
  Args:
      x (torch.Tensor): A Tensor of shape :math:`(N, H, W, C)`.
  
  Returns:
      torch.Tensor: A Tensor of shape :math:`(N, H, W, C)`.
docstr_tok: ["Forward","function",".","Args",":","x","(","torch.Tensor",")",":","A","Tensor","of","shape",":","math",":","`","(","N",",","H",",","W",",","C",")","`",".","Returns",":","torch.Tensor",":","A","Tensor","of","shape",":","math",":","`","(","N",",","H",",","W",",","C",")","`","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Forward function.
  
      Args:
          x (torch.Tensor): A Tensor of shape :math:`(N, H, W, C)`.
  
      Returns:
          torch.Tensor: A Tensor of shape :math:`(N, H, W, C)`.
      """
      x = self.fc1(x)
      x = self.act(x)
      x = self.drop(x)
      x = self.fc2(x)
      x = self.drop(x)
      return x
  
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 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: x