class_docstr: This wrapper handles torch models for image classification.
  
  It can either support models from torchvision.models (resnet18, alexnet, ...) or a
  simple CNN for MNIST (simple-cnn, see SimpleCnn class).
class_docstr_tok: ["This","wrapper","handles","torch","models","for","image","classification",".","It","can","either","support","models","from","torchvision.models","(","resnet18",",","alexnet",",","...",")","or","a","simple","CNN","for","MNIST","(","simple-cnn",",","see","SimpleCnn","class",")","."]
docstr: Loads the model from file.
docstr_tok: ["Loads","the","model","from","file","."]
code: def _load(self) -> None:
      """Loads the model from file."""
      self.model = torch.load(self.out_dir / "model.pt")
  
imports: import comet_ml
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.data import DataLoader
  import torchvision
  from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
  from ignite.metrics import Accuracy, Loss
  import numpy
  from loguru import logger
  from ..model_wrapper import ModelWrapper
  from .None import preprocessing, visualization
  from ..None import utils
label_desc: Use image node_id 5 for calling the TorchImageClassificationWrapper obj's underlying member method code with example usage: obj._load() without return types