code: def validate_one_epoch(self, data_loader):
      self.eval()
      self.model_state = enums.ModelState.VALID
      losses = AverageMeter()
      if self.using_tpu:
          tk0 = data_loader
      else:
          tk0 = tqdm(data_loader, total=len(data_loader))
      for b_idx, data in enumerate(tk0):
          self.train_state = enums.TrainingState.VALID_STEP_START
          with torch.no_grad():
              loss, metrics = self.validate_one_step(data)
          self.train_state = enums.TrainingState.VALID_STEP_END
          losses.update(loss.item(), data_loader.batch_size)
          if b_idx == 0:
              metrics_meter = {k: AverageMeter() for k in metrics}
          monitor = {}
          for m_m in metrics_meter:
              metrics_meter[m_m].update(
                  metrics[m_m], data_loader.batch_size
              )
              monitor[m_m] = metrics_meter[m_m].avg
          if not self.using_tpu:
              tk0.set_postfix(loss=losses.avg, stage="valid", **monitor)
          self.current_valid_step += 1
      if not self.using_tpu:
          tk0.close()
      self.update_metrics(losses=losses, monitor=monitor)
      return losses.avg
  
imports: import warnings
  import psutil
  import torch
  import torch.nn
  from tqdm import tqdm
  from tez import enums
  from tez.callbacks import CallbackRunner
  from tez.utils import AverageMeter
label_desc: Use image node_id 19 for calling the Model obj's underlying member method code with example usage: obj.validate_one_epoch(data_loader) and returns: losses