code: def _run_model_one_epoch(self, train=False):
      if train:
          self.model.train()
          data_loader = self.train_data_loader
      else:
          self.model.eval()
          data_loader = self.val_data_loader
  
      running_loss = 0.0
  
      for step, (inputs, labels) in tqdm(
          enumerate(data_loader), total=len(data_loader)
      ):
          inputs = tuple(t.to(self.device) for t in inputs)
          if not isinstance(labels, torch.Tensor):
              labels = torch.tensor(labels)
              if len(labels.shape) == 0:
                  labels = labels.unsqueeze(0)
          labels = labels.to(self.device)
  
          if train:
              self.optimizer.zero_grad()
  
          outputs = self.model(
              *inputs
          )  # model returns logits and softmax as a tuple
          loss = self.criterion(outputs, labels)
  
          if train:
              loss.backward()
              self.optimizer.step()
  
          running_loss += loss.item()
  
      loss = running_loss / (len(data_loader) + 1e-5)
      return loss
  
imports: import json
  import logging
  import os.path
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Tuple, List, Any, Dict
  import torch
  from sparseml.onnx.optim import ModelAnalyzer, pruning_loss_sens_magnitude
  from sparseml.pytorch.optim import ScheduledModifierManager
  from sparseml.pytorch.sparsification import EpochRangeModifier, GMPruningModifier
  from sparseml.pytorch.utils import ModuleExporter
  from sparsify.blueprints.utils import default_epochs_distribution, PruningModelEvaluator, default_pruning_settings
  from sparsify.schemas import ProjectModelAnalysisSchema
  from torch.nn import CrossEntropyLoss, MSELoss
  from torch.optim import SGD
  from tqdm.auto import tqdm
label_desc: Use image node_id 3 for calling the PruningTrainer obj's underlying member method code with example usage: obj._run_model_one_epoch(train) and returns: loss