code: def sum_and_log_step_results(self, losses, logging=True):
      # losses is a dict of several type of losses, e.g. ['loss_cls', 'loss_conf', 'loss_xy', 'loss_wh']
      # each type of losses may have multiple channels due to multiple resolution settings
      total_loss = 0.0
      for loss_key, loss_values in losses.items():
          curr_loss = 0.0
          if isinstance(loss_values, list) or isinstance(
              loss_values, tuple
          ):  # is a collection of shape 0 tensors
              for loss_chanel_idx, loss_val in enumerate(loss_values):
                  if logging:
                      self.log(
                          f"step/{loss_key}_{loss_chanel_idx}", loss_val
                      )
                  curr_loss += loss_val
          else:  # is a tensor
              curr_loss += loss_values.sum()
  
          if logging:
              self.log(f"step/{loss_key}", curr_loss)
          total_loss += curr_loss
  
      return total_loss
  
imports: import logging
  from typing import Callable, Optional, Union
  import lightning.pytorch
  import torchmetrics
  from lightning.pytorch.utilities import grad_norm
  from torch.nn.modules.loss import _Loss
  from torchmetrics.aggregation import BaseAggregator
  from ..constants import BBOX, IMAGE, LABEL
  from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_lr_scheduler, get_optimizer, remove_parameters_without_grad
label_desc: Use image node_id 7 for calling the MMDetLitModule obj's underlying member method code with example usage: obj.sum_and_log_step_results(losses, logging) and returns: total_loss