code: def _resnet(
      arch: str,
      block: type[ResNetBlock | ResNetBottleneck],
      layers: list[int],
      block_inplanes: list[int],
      pretrained: bool | str,
      progress: bool,
      **kwargs: Any,
  ) -> ResNet:
      model: ResNet = ResNet(block, layers, block_inplanes, **kwargs)
      if pretrained:
          device = "cuda" if torch.cuda.is_available() else "cpu"
          if isinstance(pretrained, str):
              if Path(pretrained).exists():
                  logger.info(f"Loading weights from {pretrained}...")
                  model_state_dict = torch.load(
                      pretrained, map_location=device
                  )
              else:
                  # Throw error
                  raise FileNotFoundError(
                      "The pretrained checkpoint file is not found"
                  )
          else:
              # Also check bias downsample and shortcut.
              if kwargs.get("spatial_dims", 3) == 3:
                  if (
                      kwargs.get("n_input_channels", 3) == 1
                      and kwargs.get("feed_forward", True) is False
                  ):
                      search_res = re.search(r"resnet(\d+)", arch)
                      if search_res:
                          resnet_depth = int(search_res.group(1))
                      else:
                          raise ValueError(
                              "arch argument should be as 'resnet_{resnet_depth}"
                          )
  
                      # Check model bias_downsample and shortcut_type
                      (
                          bias_downsample,
                          shortcut_type,
                      ) = get_medicalnet_pretrained_resnet_args(
                          resnet_depth
                      )
                      if shortcut_type == kwargs.get(
                          "shortcut_type", "B"
                      ) and (
                          bool(bias_downsample)
                          == kwargs.get("bias_downsample", False)
                          if bias_downsample != -1
                          else True
                      ):
                          # Download the MedicalNet pretrained model
                          model_state_dict = (
                              get_pretrained_resnet_medicalnet(
                                  resnet_depth,
                                  device=device,
                                  datasets23=True,
                              )
                          )
                      else:
                          raise NotImplementedError(
                              f"Please set shortcut_type to {shortcut_type} and bias_downsample to"
                              f"{bool(bias_downsample) if bias_downsample!=-1 else 'True or False'}"
                              f"when using pretrained MedicalNet resnet{resnet_depth}"
                          )
                  else:
                      raise NotImplementedError(
                          "Please set n_input_channels to 1"
                          "and feed_forward to False in order to use MedicalNet pretrained weights"
                      )
              else:
                  raise NotImplementedError(
                      "MedicalNet pretrained weights are only avalaible for 3D models"
                  )
          model_state_dict = {
              key.replace("module.", ""): value
              for key, value in model_state_dict.items()
          }
          model.load_state_dict(model_state_dict, strict=True)
      return model
  
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 3 for calling a global function with example usage: _resnet(arch, block, layers, block_inplanes, pretrained, progress) and returns: model