class_docstr: This class defines the training and validation code for training models with CVNets
code: def run_loss_landscape(self):
      # Loss landscape code is adapted from https://github.com/xxxnell/how-do-vits-work
      ll_start_time = time.time()
      try:
          n_points = getattr(self.opts, "loss_landscape.n_points", 32)
          min_x = getattr(self.opts, "loss_landscape.min_x", -1.0)
          max_x = getattr(self.opts, "loss_landscape.max_x", 1.0)
          min_y = getattr(self.opts, "loss_landscape.min_y", -1.0)
          max_y = getattr(self.opts, "loss_landscape.max_y", 1.0)
  
          if self.is_master_node:
              logger.log(
                  "Loss landscape coord space params: \n\tmin_x={}\n\tmax_x={}\n\tmin_y={}\n\tmax_y={}\n\tn_points={}".format(
                      min_x, max_x, min_y, max_y, n_points
                  )
              )
  
          ll_metrics = ["loss"]
          ll_stats = Statistics(
              opts=self.opts,
              metric_names=ll_metrics,
              is_master_node=self.is_master_node,
              is_distributed=self.use_distributed,
              log_writers=self.log_writers,
          )
          has_module = hasattr(self.model, "module")
          unwrapped_model = unwrap_model_fn(self.model)
          model_name = unwrapped_model.__class__.__name__
  
          # copy the model and create bases
          model = copy.deepcopy(self.model)
          weight_state_0 = unwrapped_model.state_dict()
          bases = ll_utils.create_bases(
              model=model, device=self.device, has_module=has_module
          )
  
          xs = np.linspace(min_x, max_x, n_points)
          ys = np.linspace(min_y, max_y, n_points)
  
          grid_a, grid_b = np.meshgrid(xs, ys, indexing="xy")
          loss_surface = np.empty_like(grid_a)
  
          epoch = -1
          for coord_a, coord_b in product(
              range(n_points), range(n_points)
          ):
              epoch += 1
              coords_list = [
                  grid_a[coord_a, coord_b],
                  grid_b[coord_a, coord_b],
              ]
              weight_state_1 = copy.deepcopy(weight_state_0)
              gs = [
                  {k: r * bs[k] for k in bs}
                  for r, bs in zip(coords_list, bases)
              ]
              gs = {
                  k: torch.sum(torch.stack([g[k] for g in gs]), dim=0)
                  + weight_state_1[k]
                  for k in gs[0]
              }
  
              # load the weights
              unwrapped_model.load_state_dict(gs)
  
              model = model.to(device=self.device)
              model.eval()
  
              total_samples = len(self.val_loader)
              with torch.no_grad():
                  epoch_start_time = time.time()
                  processed_samples = 0
                  for batch_id, batch in enumerate(self.val_loader):
                      batch = move_to_device(
                          opts=self.opts, x=batch, device=self.device
                      )
                      samples, targets = (
                          batch["samples"],
                          batch["targets"],
                      )
  
                      batch_size = get_batch_size(samples)
                      processed_samples += batch_size
  
                      # make the prediction and compute loss
                      pred_label = model(samples)
                      loss_dict_or_tensor: Union[
                          Dict, Tensor
                      ] = self.criteria(
                          input_sample=samples,
                          prediction=pred_label,
                          target=targets,
                      )
  
                      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.")
  
                      ll_stats.update(
                          pred_label=pred_label,
                          target_label=targets,
                          extras={"loss": loss_dict_or_tensor},
                          batch_time=0.0,
                          batch_size=batch_size,
                      )
  
                      if (
                          batch_id % self.log_freq == 0
                          and self.is_master_node
                      ):
                          ll_stats.iter_summary(
                              epoch=epoch,
                              n_processed_samples=processed_samples,
                              total_samples=total_samples,
                              elapsed_time=epoch_start_time,
                              learning_rate=0.0,
                          )
  
                  avg_loss = ll_stats.avg_statistics(
                      metric_name="loss", sub_metric_name="total_loss"
                  )
                  loss_surface[coord_a, coord_b] = avg_loss
                  if self.is_master_node:
                      print(
                          "x: {:.2f}, y: {:.2f}, loss: {:.2f}".format(
                              coords_list[0], coords_list[1], avg_loss
                          )
                      )
  
                  if self.is_master_node:
                      lr_list = [0.0]
  
                      for log_writer in self.log_writers:
                          log_metrics(
                              lrs=lr_list,
                              log_writer=log_writer,
                              train_loss=0.0,
                              val_loss=avg_loss,
                              epoch=epoch,
                              best_metric=self.best_metric,
                              val_ema_loss=None,
                              ckpt_metric_name=None,
                              train_ckpt_metric=None,
                              val_ckpt_metric=None,
                              val_ema_ckpt_metric=None,
                          )
  
                  gc.collect()
                  # take a small nap
                  time.sleep(if_test_env(0, otherwise=1))
  
          if self.is_master_node:
              ll_utils.plot_save_graphs(
                  save_dir=self.save_location,
                  model_name=model_name,
                  grid_a=grid_a,
                  grid_b=grid_b,
                  loss_surface=loss_surface,
                  resolution=n_points,
              )
      except KeyboardInterrupt as e:
          if self.is_master_node:
              logger.log(
                  "Keyboard interruption. Exiting from early training"
              )
              raise e
      except Exception as e:
          if "out of memory" in str(e):
              logger.log("OOM exception occured")
              n_gpus = getattr(self.opts, "dev.num_gpus", 1)
              for dev_id in range(n_gpus):
                  mem_summary = torch.cuda.memory_summary(
                      device=torch.device("cuda:{}".format(dev_id)),
                      abbreviated=True,
                  )
                  logger.log(
                      "Memory summary for device id: {}".format(dev_id)
                  )
                  print(mem_summary)
          else:
              logger.log(
                  "Exception occurred that interrupted the training. {}".format(
                      str(e)
                  )
              )
              print(e)
              raise e
      finally:
          if self.use_distributed:
              torch.distributed.destroy_process_group()
  
          torch.cuda.empty_cache()
  
          if self.is_master_node:
              ll_end_time = time.time()
              hours, rem = divmod(ll_end_time - ll_start_time, 3600)
              minutes, seconds = divmod(rem, 60)
              train_time_str = "{:0>2}:{:0>2}:{:05.2f}".format(
                  int(hours), int(minutes), seconds
              )
              logger.log(
                  "Loss landspace evaluation took {}".format(
                      train_time_str
                  )
              )
  
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 8 for calling the Trainer obj's underlying member method code with example usage: obj.run_loss_landscape() without return types