code: def resnet50(
      pretrained: bool = False, progress: bool = True, **kwargs: Any
  ) -> ResNet:
      """ResNet-50 with optional pretrained support when `spatial_dims` is 3.
  
      Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on 23 medical datasets
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet(
          "resnet50",
          ResNetBottleneck,
          [3, 4, 6, 3],
          get_inplanes(),
          pretrained,
          progress,
          **kwargs,
      )
  
imports: from __future__ import annotations
  import logging
  import re
  from collections.abc import Callable
  from functools import partial
  from pathlib import Path
  from typing import Any
  import torch
  import torch.nn
  from monai.networks.layers.factories import Conv, Norm, Pool
  from monai.networks.layers.utils import get_pool_layer
  from monai.utils import ensure_tuple_rep
  from monai.utils.module import look_up_option, optional_import
label_desc: Use image node_id 7 for calling a global function with example usage: resnet50(pretrained, progress) and returns: _resnet