code: def db_resnet50(pretrained: bool = False, **kwargs: Any) -> DBNet:
      """DBNet as described in `"Real-time Scene Text Detection with Differentiable Binarization"
      <https://arxiv.org/pdf/1911.08947.pdf>`_, using a ResNet-50 backbone.
  
      >>> import torch
      >>> from doctr.models import db_resnet50
      >>> model = db_resnet50(pretrained=True)
      >>> input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32)
      >>> out = model(input_tensor)
  
      Args:
      ----
          pretrained (bool): If True, returns a model pre-trained on our text detection dataset
          **kwargs: keyword arguments of the DBNet architecture
  
      Returns:
      -------
          text detection architecture
      """
      return _dbnet(
          "db_resnet50",
          pretrained,
          resnet50,
          ["layer1", "layer2", "layer3", "layer4"],
          None,
          ignore_keys=[
              "prob_head.6.weight",
              "prob_head.6.bias",
              "thresh_head.6.weight",
              "thresh_head.6.bias",
          ],
          **kwargs,
      )
  
imports: from typing import Any, Callable, Dict, List, Optional
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision.models import resnet34, resnet50
  from torchvision.models._utils import IntermediateLayerGetter
  from torchvision.ops.deform_conv import DeformConv2d
  from doctr.file_utils import CLASS_NAME
  from ...classification import mobilenet_v3_large
  from ...utils import _bf16_to_float32, load_pretrained_params
  from .base import DBPostProcessor, _DBNet
label_desc: Use image node_id 3 for calling a global function with example usage: db_resnet50(pretrained) and returns: _dbnet