code: def test_data_loader(self):
      for mll in self.mlls.values():
          if type(mll) is not ExactMarginalLogLikelihood:
              continue
  
          dataset = TensorDataset(
              *mll.model.train_inputs, mll.model.train_targets
          )
          loader = DataLoader(
              dataset, batch_size=len(mll.model.train_targets)
          )
          params = {
              n: p for n, p in mll.named_parameters() if p.requires_grad
          }
          A = get_loss_closure_with_grads(mll, params)
          (a, das) = A()
  
          B = get_loss_closure_with_grads(
              mll, params, data_loader=loader
          )
          with gpytorch_settings.debug(
              False
          ):  # disables GPyTorch's internal check
              (b, dbs) = B()
  
          self.assertTrue(a.allclose(b))
          for da, db in zip_longest(das, dbs):
              self.assertTrue(da.allclose(db))
  
      loader = DataLoader(
          mll.model.train_targets, len(mll.model.train_targets)
      )
      closure = get_loss_closure_with_grads(
          mll, params, data_loader=loader
      )
      with self.assertRaisesRegex(
          TypeError, "Expected .* a batch of tensors"
      ):
          closure()
  
file_path: botorch/test/optim/closures/test_model_closures.py
imports: from itertools import zip_longest
  from math import pi
  import torch
  from botorch.models import ModelListGP, SingleTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.optim.closures.model_closures import get_loss_closure, get_loss_closure_with_grads
  from botorch.utils.testing import BotorchTestCase
  from gpytorch import settings
  from gpytorch.mlls import ExactMarginalLogLikelihood, SumMarginalLogLikelihood
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 3 for calling the TestLossClosures obj's underlying member method code with example usage: obj.test_data_loader() without return types