class_docstr: MTCNN RNet.
  
  Keyword Arguments:
      pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
class_docstr_tok: ["MTCNN","RNet",".","Keyword","Arguments",":","pretrained","{","bool","}","--","Whether","or","not","to","load","saved","pretrained","weights","(","default",":","{","True","}",")"]
code: def forward(self, x):
      x = self.conv1(x)
      x = self.prelu1(x)
      x = self.pool1(x)
      x = self.conv2(x)
      x = self.prelu2(x)
      x = self.pool2(x)
      x = self.conv3(x)
      x = self.prelu3(x)
      x = x.permute(0, 3, 2, 1).contiguous()
      x = self.dense4(x.view(x.shape[0], -1))
      x = self.prelu4(x)
      a = self.dense5_1(x)
      a = self.softmax5_1(a)
      b = self.dense5_2(x)
      return b, a
  
imports: import torch
  from torch import nn
  import numpy
  import os
  from .utils.detect_face import detect_face, extract_face
label_desc: Use image node_id 2 for calling the RNet obj's underlying member method code with example usage: obj.forward(x) and returns: b,  a