code: def resnet(dataset: str, depth: int, pretrained=False) -> Module:
      """
      Wrapper for (basic) renset available in the pytorchcv package. More variants
      are availble through the general wrapper.
  
      :param dataset: One of cifar10, cifar100, svhn, imagenet.
      :param depth: depth of the architecture, one of (10, 12, 14, 16, 18, 26, 34,
                    50, 101, 152, 200) for imagenet,
                    (20, 56, 110, 1001, 1202) for the other datasets.
      :param pretrained: loads model pretrained on `dataset`.
      """
  
      if dataset in ["cifar10", "cifar100", "svhn"]:
          available_depths = [20, 56, 110, 1001, 1202]
          model_name = f"resnet{depth}_{dataset}"
      elif dataset == "imagenet":
          available_depths = [
              10,
              12,
              14,
              16,
              18,
              26,
              34,
              50,
              101,
              152,
              200,
          ]
          model_name = f"resnet{depth}"
      else:
          raise ValueError(f"Unrecognized dataset {dataset}")
  
      if depth not in available_depths:
          raise ValueError(
              f"Depth {depth} not available for dataset {dataset}, "
              f"availble depths are {available_depths}"
          )
  
      model = ptcv_get_model(model_name, pretrained=pretrained)
      return model
  
imports: from pytorchcv.model_provider import get_model
  from torch.nn import Module
label_desc: Use image node_id 2 for calling a global function with example usage: resnet(dataset, depth, pretrained) and returns: model