code: def test_basic(
      self,
      num_workers: int,
      pin_memory: bool,
      batch_size: int,
      max_physical_batch_size: int,
  ):
      batches_per_step = max(1, batch_size // max_physical_batch_size)
      model, optimizer, data_loader = self._init_training(
          num_workers=num_workers,
          pin_memory=pin_memory,
          batch_size=batch_size,
      )
  
      privacy_engine = PrivacyEngine()
      model, optimizer, data_loader = privacy_engine.make_private(
          module=model,
          optimizer=optimizer,
          data_loader=data_loader,
          noise_multiplier=1.0,
          max_grad_norm=1.0,
          poisson_sampling=False,
          grad_sample_mode=self.GSM_MODE,
      )
      with BatchMemoryManager(
          data_loader=data_loader,
          max_physical_batch_size=max_physical_batch_size,
          optimizer=optimizer,
      ) as new_data_loader:
          self.assertEqual(
              len(data_loader), len(data_loader.dataset) // batch_size
          )
          self.assertEqual(
              len(new_data_loader),
              len(data_loader.dataset) // max_physical_batch_size,
          )
          weights_before = torch.clone(model._module.fc.weight)
          for i, (x, y) in enumerate(new_data_loader):
              self.assertTrue(x.shape[0] <= max_physical_batch_size)
  
              out = model(x)
              loss = (y - out).mean()
  
              loss.backward()
              optimizer.step()
              optimizer.zero_grad()
  
              if (i + 1) % batches_per_step > 0:
                  self.assertTrue(
                      torch.allclose(
                          model._module.fc.weight, weights_before
                      )
                  )
              else:
                  self.assertFalse(
                      torch.allclose(
                          model._module.fc.weight, weights_before
                      )
                  )
                  weights_before = torch.clone(model._module.fc.weight)
  
imports: import unittest
  import torch
  import torch.nn
  from hypothesis import HealthCheck, given, settings
  from hypothesis import strategies
  from opacus import PrivacyEngine
  from opacus.utils.batch_memory_manager import BatchMemoryManager
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 3 for calling the BatchMemoryManagerTest obj's underlying member method code with example usage: obj.test_basic(num_workers, pin_memory, batch_size, max_physical_batch_size) without return types