code: def pyramidnet(dataset: str, depth: int, pretrained=False) -> Module:
      """
      Wrapper for pyramidnet available in the pytorchcv package.
  
      :param dataset: One of cifar10, cifar100, svhn, imagenet.
      :param depth: The depth of the pyramidnet. For imagenet 101 is supported.
                    The other datasets support dephts (110, 164, 200, 236, 272).
      :param pretrained: load model pretrained on `dataset`..
      """
      if dataset in ["cifar10", "cifar100", "svhn"]:
          available_depths = [110, 164, 200, 236, 272]
          alpha = {110: 48, 164: 270, 200: 240, 236: 220, 272: 200}.get(
              depth
          )
          if depth < 200:
              model_name = f"pyramidnet{depth}_a{alpha}_{dataset}"
          else:
              # These models have batch normalization
              model_name = f"pyramidnet{depth}_a{alpha}_bn_{dataset}"
      elif dataset == "imagenet":
          available_depths = [101]
          alpha = 360
          model_name = f"pyramidnet{depth}_a{alpha}"
      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 4 for calling a global function with example usage: pyramidnet(dataset, depth, pretrained) and returns: model