class_docstr: Component to perform the perceptual evaluation with the networks pretrained by Chen, et al. "Med3D: Transfer
  Learning for 3D Medical Image Analysis". This class uses torch Hub to download the networks from
  "Warvito/MedicalNet-models".
  
  Args:
      net: {``"medicalnet_resnet10_23datasets"``, ``"medicalnet_resnet50_23datasets"``}
          Specifies the network architecture to use. Defaults to ``"medicalnet_resnet10_23datasets"``.
      verbose: if false, mute messages from torch Hub load function.
code: def __init__(
      self,
      net: str = "medicalnet_resnet10_23datasets",
      verbose: bool = False,
  ) -> None:
      super().__init__()
      torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
      self.model = torch.hub.load(
          "warvito/MedicalNet-models", model=net, verbose=verbose
      )
      self.eval()
  
      for param in self.parameters():
          param.requires_grad = False
  
imports: from __future__ import annotations
  import warnings
  import torch
  import torch.nn
  from monai.utils import optional_import
  from monai.utils.enums import StrEnum
label_desc: Use image node_id 1 to create a new MedicalNetPerceptualSimilarity object from inherited base classes: nn with example: obj = MedicalNetPerceptualSimilarity(net, verbose)