code: def vgg16_bn_r(pretrained: bool = False, **kwargs: Any) -> 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 tensorflow as tf
      >>> from doctr.models import vgg16_bn_r
      >>> model = vgg16_bn_r(pretrained=False)
      >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.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,
          [2, 2, 3, 3, 3],
          [64, 128, 256, 512, 512],
          [False, False, True, True, True],
          **kwargs,
      )
  
file_path: doctr/doctr/models/classification/vgg/tensorflow.py
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional, Tuple
  from tensorflow.keras import layers
  from tensorflow.keras.models import Sequential
  from doctr.datasets import VOCABS
  from ...utils import conv_sequence, 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