code: def crnn_vgg16_bn(pretrained: bool = False, **kwargs: Any) -> CRNN:
      """CRNN with a VGG-16 backbone as described in `"An End-to-End Trainable Neural Network for Image-based
      Sequence Recognition and Its Application to Scene Text Recognition" <https://arxiv.org/pdf/1507.05717.pdf>`_.
  
      >>> import torch
      >>> from doctr.models import crnn_vgg16_bn
      >>> model = crnn_vgg16_bn(pretrained=True)
      >>> input_tensor = torch.rand(1, 3, 32, 128)
      >>> out = model(input_tensor)
  
      Args:
      ----
          pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
          **kwargs: keyword arguments of the CRNN architecture
  
      Returns:
      -------
          text recognition architecture
      """
      return _crnn(
          "crnn_vgg16_bn",
          pretrained,
          vgg16_bn_r,
          ignore_keys=["linear.weight", "linear.bias"],
          **kwargs,
      )
  
imports: from copy import deepcopy
  from itertools import groupby
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from doctr.datasets import VOCABS, decode_sequence
  from ...classification import mobilenet_v3_large_r, mobilenet_v3_small_r, vgg16_bn_r
  from ...utils.pytorch import load_pretrained_params
  from ..core import RecognitionModel, RecognitionPostProcessor
label_desc: Use image node_id 2 for calling a global function with example usage: crnn_vgg16_bn(pretrained) and returns: _crnn