code: def _init_training(self, batch_size=10, **data_loader_kwargs):
      model = Model()
      optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
      data_loader = DataLoader(
          self.dataset, batch_size=batch_size, **data_loader_kwargs
      )
  
      return model, optimizer, data_loader
  
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 2 for calling the BatchMemoryManagerTest obj's underlying member method code with example usage: obj._init_training(batch_size) and returns: model,  optimizer,  data_loader