docstr: Check the metadata is returned in the expected format depending on whether
  the input `MetaTensor` is a batch of data or not.
docstr_tok: ["Check","the","metadata","is","returned","in","the","expected","format","depending","on","whether","the","input","`","MetaTensor","`","is","a","batch","of","data","or","not","."]
code: def test_indexing(self):
      """
      Check the metadata is returned in the expected format depending on whether
      the input `MetaTensor` is a batch of data or not.
      """
      ims = [self.get_im()[0] for _ in range(5)]
      data = list_data_collate(ims)
  
      # check that when using non-batch data, metadata is copied wholly when indexing
      # or iterating across data.
      im = ims[0]
      self.check_meta(im[0], im)
      self.check_meta(next(iter(im)), im)
  
      self.assertEqual(im[None].shape, (1, 1, 10, 8))
      self.assertEqual(data[None].shape, (1, 5, 1, 10, 8))
  
      # index
      d = data[0]
      self.check(d, ims[0], ids=False)
  
      # iter
      d = next(iter(data))
      self.check(d, ims[0], ids=False)
  
      # complex indexing
  
      # `is_batch==True`, should have subset of image and metadata.
      d = data[1:3]
      self.check(d, list_data_collate(ims[1:3]), ids=False)
  
      # is_batch==True, should have subset of image and same metadata as `[1:3]`.
      d = data[1:3, 0]
      self.check(
          d, list_data_collate([i[0] for i in ims[1:3]]), ids=False
      )
  
      # `is_batch==False`, should have first metadata and subset of first image.
      d = data[0, 0]
      self.check(d, ims[0][0], ids=False)
      self.assertEqual(
          d.applied_operations, ims[0][0].applied_operations
      )
  
      # `is_batch==True`, should have all metadata and subset of all images.
      d = data[:, 0]
      self.check(d, list_data_collate([i[0] for i in ims]), ids=False)
  
      # `is_batch==True`, should have all metadata and subset of all images.
      d = data[..., -1]
      self.check(
          d, list_data_collate([i[..., -1] for i in ims]), ids=False
      )
  
      # `is_batch==False`, tuple split along batch dim. Should have individual
      # metadata.
      d = data.unbind(0)
      self.assertIsInstance(d, tuple)
      self.assertEqual(len(d), len(ims))
      for _d, _im in zip(d, ims):
          self.check(_d, _im, ids=False)
  
      # `is_batch==False`, tuple split along batch dim. Should have individual
      # metadata.
      d = data.unbind(dim=0)
      self.assertIsInstance(d, tuple)
      self.assertEqual(len(d), len(ims))
      for _d, _im in zip(d, ims):
          self.check(_d, _im, ids=False)
          self.assertEqual(
              _d.applied_operations, _im.applied_operations
          )
  
      # `is_batch==True`, tuple split along non-batch dim. Should have all metadata.
      d = data.unbind(-1)
      self.assertIsInstance(d, tuple)
      self.assertEqual(len(d), ims[0].shape[-1])
      for _d in d:
          self.check_meta(_d, data)
  
      # `is_batch==True`, tuple split along non-batch dim. Should have all metadata.
      d = data.unbind(dim=-1)
      self.assertIsInstance(d, tuple)
      self.assertEqual(len(d), ims[0].shape[-1])
      for _d in d:
          self.check_meta(_d, data)
  
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 22 for calling the TestMetaTensor obj's underlying member method code with example usage: obj.test_indexing() without return types