class_docstr: This class defines the training and validation code for training models with CVNets
code: def train_epoch(self, epoch):
      time.sleep(
          if_test_env(0.5, otherwise=2)
      )  # To prevent possible deadlock during epoch transition
  
      if self.is_master_node:
          logger.double_dash_line()
          logger.debug(
              "Training epoch {} with {} samples".format(
                  epoch, self.train_loader.samples_in_dataset()
              )
          )
  
      train_stats = Statistics(
          opts=self.opts,
          metric_names=self.train_metric_names,
          is_master_node=self.is_master_node,
          is_distributed=self.use_distributed,
          log_writers=self.log_writers,
      )
  
      self.model.train()
      # criteria is also a nn.Module and we may need access to training property in some
      # loss functions. So, to enable, that, we set criteria to train/eval mode
      self.criteria.train()
  
      accum_freq = (
          self.accum_freq if epoch >= self.accum_after_epoch else 1
      )
      max_norm = getattr(self.opts, "common.grad_clip", None)
  
      # set the gradient to zero or None
      self._zero_grad()
  
      epoch_start_time = time.time()
      batch_load_start = time.time()
      grad_norm = torch.tensor(
          [0.0], dtype=torch.float, device=self.device
      )
      for batch_id, batch in enumerate(self.train_loader):
          if self.train_iterations > self.max_iterations:
              self.max_iterations_reached = True
              return -1, -1
  
          # move to device
          batch = move_to_device(
              opts=self.opts, x=batch, device=self.device
          )
          # apply mix-up transforms if any
          batch = apply_mixing_transforms(opts=self.opts, data=batch)
  
          batch_load_toc = time.time() - batch_load_start
  
          samples, targets = batch["samples"], batch["targets"]
  
          batch_size = get_batch_size(samples)
  
          # update the learning rate
          self.optimizer = self.scheduler.update_lr(
              optimizer=self.optimizer,
              epoch=epoch,
              curr_iter=self.train_iterations,
          )
  
          # adjust bn momentum
          if self.adjust_norm_mom is not None:
              self.adjust_norm_mom.adjust_momentum(
                  model=self.model,
                  epoch=epoch,
                  iteration=self.train_iterations,
              )
  
          with autocast_fn(
              enabled=self.mixed_precision_training,
              amp_precision=self.mixed_precision_dtype,
          ):
              # prediction
              pred_label = self.model(samples)
              # compute loss
              loss_dict_or_tensor: Union[Dict, Tensor] = self.criteria(
                  input_sample=samples,
                  prediction=pred_label,
                  target=targets,
                  epoch=epoch,
                  iterations=self.train_iterations,
              )
  
              if isinstance(loss_dict_or_tensor, Dict):
                  if "total_loss" not in loss_dict_or_tensor.keys():
                      logger.error(
                          "total_loss key is required for loss functions that return outputs as dictionary."
                      )
                  loss = loss_dict_or_tensor["total_loss"]
              elif isinstance(loss_dict_or_tensor, Tensor):
                  loss = loss_dict_or_tensor
              else:
                  logger.error(
                      "Loss value should be an instance of Tensor or Dict"
                  )
  
              if isinstance(loss, torch.Tensor) and torch.isnan(loss):
                  logger.error("Nan encountered in the loss.")
  
          # perform the backward pass with gradient accumulation [Optional]
          self.gradient_scaler.scale(loss).backward()
  
          if (batch_id + 1) % accum_freq == 0:
              if max_norm is not None:
                  # For gradient clipping, unscale the gradients and then clip them
                  self.gradient_scaler.unscale_(self.optimizer)
                  torch.nn.utils.clip_grad_norm_(
                      self.model.parameters(), max_norm=max_norm
                  )
  
              if "grad_norm" in self.train_metric_names:
                  # compute grad_norm for logging purposes.
                  # We can't use the output of clip_grad_norm_ because it returns the total norm before clipping
                  grad_norm = self.compute_grad_norm()
  
              # optimizer step
              self.gradient_scaler.step(optimizer=self.optimizer)
              # update the scale for next batch
              self.gradient_scaler.update()
              # set the gradient to zero or None
              self._zero_grad()
  
              self.train_iterations += 1
  
              if self.model_ema is not None:
                  self.model_ema.update_parameters(self.model)
  
          train_stats.update(
              pred_label=pred_label,
              target_label=targets,
              extras={
                  "loss": loss_dict_or_tensor,
                  "grad_norm": grad_norm,
              },
              batch_time=batch_load_toc,
              batch_size=batch_size,
          )
  
          # save the checkpoint every N updates
          if (
              self.save_interval
              and (self.train_iterations % self.save_interval_freq) == 0
          ):
              save_interval_checkpoint(
                  iterations=self.train_iterations,
                  epoch=epoch,
                  model=self.model,
                  optimizer=self.optimizer,
                  best_metric=loss.item(),
                  save_dir=self.save_location,
                  gradient_scaler=self.gradient_scaler,
                  model_ema=self.model_ema,
              )
              logger.info(
                  "Checkpoints saved after {} updates at: {}".format(
                      self.train_iterations, self.save_location
                  ),
                  print_line=True,
              )
  
          if batch_id % self.log_freq == 0 and self.is_master_node:
              lr = self.scheduler.retrieve_lr(self.optimizer)
              train_stats.iter_summary(
                  epoch=epoch,
                  n_processed_samples=self.train_iterations,
                  total_samples=self.max_iterations,
                  learning_rate=lr,
                  elapsed_time=epoch_start_time,
              )
  
          batch_load_start = time.time()
  
      avg_loss = train_stats.avg_statistics(
          metric_name="loss", sub_metric_name="total_loss"
      )
      train_stats.epoch_summary(epoch=epoch, stage="training")
      avg_ckpt_metric = train_stats.avg_statistics(
          metric_name=self.ckpt_metric,
          sub_metric_name=self.ckpt_submetric,
      )
  
      gc.collect()
  
      return avg_loss, avg_ckpt_metric
  
imports: import copy
  import gc
  import shutil
  import time
  import traceback
  from itertools import product
  from typing import Dict, Union
  import numpy
  import torch
  from torch import Tensor
  from torch.nn import functional
  from common import DEFAULT_EPOCHS, DEFAULT_ITERATIONS, DEFAULT_LOG_FREQ, if_test_env
  from data.transforms.image_torch import apply_mixing_transforms
  from engine.utils import autocast_fn, get_batch_size, get_log_writers, log_metrics
  from loss_landscape import landscape_utils
  from metrics.stats import Statistics
  from options.parse_args import parse_validation_metric_names
  from utils import logger
  from utils.checkpoint_utils import copy_weights, save_checkpoint, save_interval_checkpoint
  from utils.common_utils import move_to_device, unwrap_model_fn
  from utils.ddp_utils import dist_barrier, is_master
  from utils.tensor_utils import reduce_tensor_sum
label_desc: Use image node_id 4 for calling the Trainer obj's underlying member method code with example usage: obj.train_epoch(epoch) and returns: avg_loss,  avg_ckpt_metric, unknown,  unknown