code: def test_collate(self, device, dtype):
      numel = 3
      ims = [
          self.get_im(device=device, dtype=dtype)[0]
          for _ in range(numel)
      ]
      ims = [
          MetaTensor(im, applied_operations=[f"t{i}"])
          for i, im in enumerate(ims)
      ]
      collated = list_data_collate(ims)
      # tensor
      self.assertIsInstance(collated, MetaTensor)
      expected_shape = (numel,) + tuple(ims[0].shape)
      self.assertTupleEqual(tuple(collated.shape), expected_shape)
      for i, im in enumerate(ims):
          self.check(im, ims[i], ids=True)
      # affine
      self.assertIsInstance(collated.affine, torch.Tensor)
      expected_shape = (numel,) + tuple(ims[0].affine.shape)
      self.assertTupleEqual(
          tuple(collated.affine.shape), expected_shape
      )
      self.assertEqual(len(collated.applied_operations), numel)
  
imports: from __future__ import annotations
  import io
  import os
  import random
  import string
  import tempfile
  import unittest
  import warnings
  from copy import deepcopy
  from multiprocessing.reduction import ForkingPickler
  import numpy
  import torch
  import torch.multiprocessing
  from parameterized import parameterized
  from monai import config
  from monai.data import DataLoader, Dataset
  from monai.data.meta_obj import get_track_meta, set_track_meta
  from monai.data.meta_tensor import MetaTensor
  from monai.data.utils import decollate_batch, list_data_collate
  from monai.transforms import BorderPadd, Compose, DivisiblePadd, FromMetaTensord, ToMetaTensord
  from monai.utils.enums import PostFix
  from monai.utils.module import pytorch_after
  from tests.utils import TEST_DEVICES, SkipIfBeforePyTorchVersion, assert_allclose, skip_if_no_cuda
label_desc: Use image node_id 19 for calling the TestMetaTensor obj's underlying member method code with example usage: obj.test_collate(device, dtype) without return types