code: def _step(self, batch, validation):
      inputs = {"x": batch[self.image_col].reshape(self.input_shape)}
      label = batch[self.label_col]
      output = self(**inputs)
  
      if self.backbone.startswith("inception") and not validation:
          # https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958/9
          loss1 = self.loss_fn(output.logits, label.long())
          loss2 = self.loss_fn(output.aux_logits, label.long())
          loss = loss1 + 0.4 * loss2
      else:
          loss = self.loss_fn(output, label.long())
      return loss
  
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 8 for calling the LitDeepVisionModel obj's underlying member method code with example usage: obj._step(batch, validation) and returns: loss