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: Returns the raw model object.
docstr_tok: ["Returns","the","raw","model","object","."]
code: def raw(self) -> dict:
      """Returns the raw model object."""
      return {"model": self.model}
  
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 7 for calling the TorchImageClassificationWrapper obj's underlying member method code with example usage: obj.raw() and returns: dict