class_docstr: MTCNN ONet.
  
  Keyword Arguments:
      pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
class_docstr_tok: ["MTCNN","ONet",".","Keyword","Arguments",":","pretrained","{","bool","}","--","Whether","or","not","to","load","saved","pretrained","weights","(","default",":","{","True","}",")"]
code: def __init__(self, pretrained=True):
      super().__init__()
  
      self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
      self.prelu1 = nn.PReLU(32)
      self.pool1 = nn.MaxPool2d(3, 2, ceil_mode=True)
      self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
      self.prelu2 = nn.PReLU(64)
      self.pool2 = nn.MaxPool2d(3, 2, ceil_mode=True)
      self.conv3 = nn.Conv2d(64, 64, kernel_size=3)
      self.prelu3 = nn.PReLU(64)
      self.pool3 = nn.MaxPool2d(2, 2, ceil_mode=True)
      self.conv4 = nn.Conv2d(64, 128, kernel_size=2)
      self.prelu4 = nn.PReLU(128)
      self.dense5 = nn.Linear(1152, 256)
      self.prelu5 = nn.PReLU(256)
      self.dense6_1 = nn.Linear(256, 2)
      self.softmax6_1 = nn.Softmax(dim=1)
      self.dense6_2 = nn.Linear(256, 4)
      self.dense6_3 = nn.Linear(256, 10)
  
      self.training = False
  
      if pretrained:
          state_dict_path = os.path.join(
              os.path.dirname(__file__), "../data/onet.pt"
          )
          state_dict = torch.load(state_dict_path)
          self.load_state_dict(state_dict)
  
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 1 to create a new ONet object from inherited base classes: nn with example: obj = ONet(pretrained)