code: def linknet_resnet50(
      pretrained: bool = False, **kwargs: Any
  ) -> LinkNet:
      """LinkNet as described in `"LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation"
      <https://arxiv.org/pdf/1707.03718.pdf>`_.
  
      >>> import tensorflow as tf
      >>> from doctr.models import linknet_resnet50
      >>> model = linknet_resnet50(pretrained=True)
      >>> input_tensor = tf.random.uniform(shape=[1, 1024, 1024, 3], maxval=1, dtype=tf.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 LinkNet architecture
  
      Returns:
      -------
          text detection architecture
      """
      return _linknet(
          "linknet_resnet50",
          pretrained,
          resnet50,
          [
              "conv2_block3_out",
              "conv3_block4_out",
              "conv4_block6_out",
              "conv5_block3_out",
          ],
          **kwargs,
      )
  
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import Model, Sequential, layers
  from doctr.file_utils import CLASS_NAME
  from doctr.models.classification import resnet18, resnet34, resnet50
  from doctr.models.utils import IntermediateLayerGetter, _bf16_to_float32, conv_sequence, load_pretrained_params
  from doctr.utils.repr import NestedObject
  from .base import LinkNetPostProcessor, _LinkNet
label_desc: Use image node_id 5 for calling a global function with example usage: linknet_resnet50(pretrained) and returns: _linknet