code: def get_return_nodes(backbone: str) -> list[str]:
      """Get the return nodes for a given backbone.
  
      Args:
          backbone (str): The name of the backbone. Must be one of
              {"resnet18", "wide_resnet50_2", "vgg19_bn", "efficientnet_b5"}.
  
      Raises:
          NotImplementedError: If the backbone is "efficientnet_b5".
          ValueError: If the backbone is not one of the supported backbones.
  
      Returns:
          list[str]: A list of return nodes for the given backbone.
      """
      if backbone == "efficientnet_b5":
          raise NotImplementedError(
              "EfficientNet feature extractor has not implemented yet."
          )
  
      return_nodes: list[str]
      if backbone in ("resnet18", "wide_resnet50_2"):
          return_nodes = ["layer1", "layer2", "layer3"]
      elif backbone == "vgg19_bn":
          return_nodes = ["features.25", "features.38", "features.52"]
      else:
          raise ValueError(
              f"Backbone {backbone} is not supported. Supported backbones are {SUPPORTED_BACKBONES}."
          )
      return return_nodes
  
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  import torchvision
  from einops import rearrange
  from sklearn.cluster import KMeans
  from torch import Tensor, nn
  from torch.fx.graph_module import GraphModule
  from torch.nn.common_types import _size_2_t
  from torch.utils.data import DataLoader
  from torchvision.models.feature_extraction import create_feature_extractor
  from tqdm import tqdm
  from anomalib.models.cfa.anomaly_map import AnomalyMapGenerator
  from anomalib.models.components import DynamicBufferModule
  from anomalib.models.components.feature_extractors import dryrun_find_featuremap_dims
label_desc: Use image node_id 1 for calling a global function with example usage: get_return_nodes(backbone) and returns: return_nodes