code: def vgg16_bn_r(pretrained: bool = False, **kwargs: Any) -> tv_vgg.VGG:
      """VGG-16 architecture as described in `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
      <https://arxiv.org/pdf/1409.1556.pdf>`_, modified by adding batch normalization, rectangular pooling and a simpler
      classification head.
  
      >>> import torch
      >>> from doctr.models import vgg16_bn_r
      >>> model = vgg16_bn_r(pretrained=False)
      >>> input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32)
      >>> out = model(input_tensor)
  
      Args:
      ----
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          **kwargs: keyword arguments of the VGG architecture
  
      Returns:
      -------
          VGG feature extractor
      """
      return _vgg(
          "vgg16_bn_r",
          pretrained,
          "vgg16_bn",
          3,
          ignore_keys=["classifier.weight", "classifier.bias"],
          **kwargs,
      )
  
file_path: doctr/doctr/models/classification/vgg/pytorch.py
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional
  from torch import nn
  from torchvision.models import vgg
  from doctr.datasets import VOCABS
  from ...utils import load_pretrained_params
label_desc: Use image node_id 2 for calling a global function with example usage: vgg16_bn_r(pretrained) and returns: _vgg