code: def get_medicalnet_pretrained_resnet_args(resnet_depth: int):
      """
      Return correct shortcut_type and bias_downsample
      for pretrained MedicalNet weights according to resnet depth
      """
      # After testing
      # False: 10, 50, 101, 152, 200
      # Any: 18, 34
      bias_downsample = (
          -1 if resnet_depth in [18, 34] else 0
      )  # 18, 10, 34
      shortcut_type = "A" if resnet_depth in [18, 34] else "B"
      return bias_downsample, shortcut_type
  
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 12 for calling a global function with example usage: get_medicalnet_pretrained_resnet_args(resnet_depth) and returns: bias_downsample,  shortcut_type