code: def train_step(
      model, iterator, device, criterion, batch_size, optimizer
  ):
      model.train()
      y_preds = []
      losses = []
      batch_sizes = []
      tic = time.time()
      for Xi, yi in iterator:
          Xi, yi = to_device(Xi, device), to_device(yi, device)
          optimizer.zero_grad()
          y_pred = model(Xi)
          y_pred = torch.log(y_pred)
          loss = criterion(y_pred, yi)
          loss.backward()
          optimizer.step()
  
          y_preds.append(y_pred)
          losses.append(loss.item())
          batch_sizes.append(len(Xi))
      toc = time.time()
      return {
          "losses": losses,
          "batch_sizes": batch_sizes,
          "y_proba": torch.cat(y_preds).cpu().detach().numpy(),
          "time": toc - tic,
      }
  
imports: import argparse
  import time
  import numpy
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import StratifiedKFold
  from sklearn.metrics import accuracy_score
  from sklearn.utils import shuffle
  import torch
  from torch import nn
  from skorch.utils import to_device
  from skorch import NeuralNetClassifier
  from skorch.callbacks import EpochScoring
label_desc: Use image node_id 5 for calling a global function with example usage: train_step(model, iterator, device, criterion, batch_size, optimizer) and returns: dict