code: def after_training_loop(self) -> None:
      logger.info("Stepping into final validation check")
      # Only do when run_type has train as it shouldn't happen on validation and
      # inference runs. Inference will take care of this anyways. Also, don't run
      # if current iteration is divisble by snapshot interval as it will just
      # be a repeat
      if (
          "train" in self.run_type
          and "val" in self.run_type
          and self.num_updates
          % self.training_config.evaluation_interval
          != 0
      ):
          # Create a new meter for this case
          report, meter = self.evaluation_loop("val")
  
          # Validation end callbacks
          self.on_validation_end(report=report, meter=meter)
  
imports: import gc
  import logging
  from abc import ABC
  from typing import Any, Dict
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.common.report import Report
  from mmf.common.sample import to_device
  from mmf.utils.distributed import is_xla
  from mmf.utils.general import clip_gradients, extract_loss, get_max_updates
  from torch import Tensor
label_desc: Use image node_id 2 for calling the TrainerTrainingLoopMixin obj's underlying member method code with example usage: obj.after_training_loop() without return types