class_docstr: A mini VGG backbone for text recognition, modified from `VGG-VeryDeep.
  
  <https://arxiv.org/pdf/1409.1556.pdf>`_
  
  Args:
      leaky_relu (bool): Use leakyRelu or not.
      input_channels (int): Number of channels of input image tensor.
docstr: Args:
      x (Tensor): Images of shape :math:`(N, C, H, W)`.
  
  Returns:
      Tensor: The feature Tensor of shape :math:`(N, 512, H/32, (W/4+1)`.
docstr_tok: ["Args",":","x","(","Tensor",")",":","Images","of","shape",":","math",":","`","(","N",",","C",",","H",",","W",")","`",".","Returns",":","Tensor",":","The","feature","Tensor","of","shape",":","math",":","`","(","N",",","512",",","H\/32",",","(","W\/4+1",")","`","."]
code: def forward(self, x):
      """
      Args:
          x (Tensor): Images of shape :math:`(N, C, H, W)`.
  
      Returns:
          Tensor: The feature Tensor of shape :math:`(N, 512, H/32, (W/4+1)`.
      """
      output = self.cnn(x)
  
      return output
  
imports: import torch.nn
  from mmengine.model import BaseModule, Sequential
  from mmocr.registry import MODELS
label_desc: Use image node_id 3 for calling the MiniVGG obj's underlying member method code with example usage: obj.forward(x) and returns: output