code: def _check_params(self):
      # TORCHVISION
      if self.backbone in models.__dict__:
          self.model = self._retry_download(
              lambda: models.__dict__[self.backbone](pretrained=True), 5
          )
      # TODO: add HUGGINGFACE.TRANSFORMERS
      else:
          raise ValueError("No model: {} found".format(self.backbone))
  
      if (
          self.additional_layers_to_train < 0
          or self.additional_layers_to_train > 3
      ):
          raise ValueError(
              "additional_layers_to_train has to between 0 and 3: {} found".format(
                  self.additional_layers_to_train
              )
          )
  
      if self.loss_name.lower() not in F.__dict__:
          raise ValueError(
              "No loss function: {} found".format(self.loss_name)
          )
      self.loss_fn = F.__dict__[self.loss_name.lower()]
  
      optimizers_mapping = {
          key.lower(): value
          for key, value in optim.__dict__.items()
          if inspect.isclass(value)
          and issubclass(value, optim.Optimizer)
      }
      if self.optimizer_name.lower() not in optimizers_mapping:
          raise ValueError(
              "No optimizer: {} found".format(self.optimizer_name)
          )
      self.optimizer_fn = optimizers_mapping[
          self.optimizer_name.lower()
      ]
  
imports: import inspect
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from pytorch_lightning.utilities import _module_available
  from urllib.error import URLError
  import time
label_desc: Use image node_id 3 for calling the LitDeepVisionModel obj's underlying member method code with example usage: obj._check_params() without return types