code: def train(
      num_epochs,
      model,
      dataloader,
      optimizer,
      accelerator,
      scheduler=None,
  ):
      "Trains for `num_epochs`"
      rands = []
      for epoch in range(num_epochs):
          # Train quickly
          model.train()
          for batch in dataloader:
              x, y = batch
              outputs = model(x)
              loss = torch.nn.functional.mse_loss(outputs, y)
              accelerator.backward(loss)
              optimizer.step()
              optimizer.zero_grad()
          rands.append(random.random())  # Introduce some randomness
          if scheduler is not None:
              scheduler.step()
      return rands
  
imports: import inspect
  import logging
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import uuid
  from contextlib import contextmanager
  import pytest
  import torch
  from parameterized import parameterized_class
  from torch import nn
  from torch.utils.data import DataLoader, TensorDataset
  from accelerate import Accelerator
  from accelerate.test_utils import device_count, execute_subprocess_async, require_non_cpu
  from accelerate.utils import ProjectConfiguration, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: train(num_epochs, model, dataloader, optimizer, accelerator, scheduler) and returns: rands