code: def train_step(self, data, optimizer):
      images, labels = data
      predicts = self(images)  # -> self.__call__() -> self.forward()
      loss = self.loss_fn(predicts, labels)
      return {"loss": loss}
  
file_path: clearml/examples/frameworks/openmmlab/openmmlab_cifar10.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision.transforms
  from mmcv.parallel import MMDataParallel
  from mmcv.runner import EpochBasedRunner
  from mmcv.utils import get_logger
  from torch.utils.data import DataLoader
  from torchvision.datasets import CIFAR10
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.train_step(data, optimizer) and returns: dict