code: def train(
      nrounds,
      model,
      optimizer,
      aux_losses=[],
      alpha=1.0,
      require_grads=False,
      require_attributions=False,
      iterator=tqdm,
      k=1,
  ):
      # Cache model state
      init_state = model.training
  
      # Set up
      results = []
      best_model = None
      best_val = float("-inf")
      itr = iterator(range(nrounds))
      for t in itr:
          # Train mode
          model.train()
  
          # Get train predictions
          if require_attributions:
              train_pred, train_shap = model(
                  Xtrain_tensor, shap_values=True, k=k
              )
          else:
              train_pred = model(Xtrain_tensor)
              train_shap = None
          # Change this line if logit gradients are desired!
          train_grads = (
              get_grads_ross(model, Xtrain_tensor)
              if require_grads
              else None
          )
  
          # Base losses
          loss = loss_fn(train_pred, ytrain_tensor)
          total_loss = loss
  
          # Extra losses
          aux_loss_results = [
              aux_loss(model, train_shap, train_grads)
              for aux_loss in aux_losses
          ]
          aux_loss_values = [r.item() for r in aux_loss_results]
          for r in aux_loss_results:
              total_loss += alpha * r
  
          # Eval mode
          model.eval()
  
          # Validation predictions
          valid_pred = model(Xvalid_tensor).detach().cpu().numpy()
          val_loss = metrics.roc_auc_score(
              yvalid_tensor.detach().cpu().numpy(), valid_pred
          )
  
          # Cache results
          results.append(
              tuple([loss.item(), val_loss] + aux_loss_values)
          )
          if val_loss > best_val:
              best_model = copy.deepcopy(model)
              best_val = val_loss
  
          # Update progress bar
          aux_string = "|".join(
              [f"{aux:.2f}" for aux in aux_loss_values]
          )
          if iterator is tqdm:
              desc = f"Trn:{loss.item():.2f}/Val:{val_loss:.2f}/Aux:{aux_string}"
              itr.set_description(desc)
  
          # Update model
          optimizer.zero_grad()
          total_loss.backward()
          optimizer.step()
      if init_state:
          best_model.train()
      else:
          best_model.eval()
      return (best_model, best_val, np.array(results))
  
imports: import os, re, copy, pickle, sys
  from collections import defaultdict
  from tqdm import tqdm
  from functools import partial
  import numpy
  import pandas
  from scipy import linalg, special
  from sklearn import impute, preprocessing, model_selection, metrics, linear_model, datasets, pipeline
  import xgboost
  import torch
  import shap
  from attributionpriors.pytorch_ops import ExpectedGradientsModel
  from matplotlib import pyplot
label_desc: Use image node_id 23 for calling a global function with example usage: train(nrounds, model, optimizer, aux_losses, alpha, require_grads, require_attributions, iterator, k) and returns: best_model,  best_val,  np