code: def test_get_item(
      self,
      num_volumes=5,
      num_channels=4,
      volume_size=(10, 13, 8),
      dtype=torch.float32,
  ):
      device = torch.device("cuda:0")
  
      # make sure we have at least 3 volumes to prevent indexing crash
      num_volumes = max(num_volumes, 3)
  
      features = torch.randn(
          size=[num_volumes, num_channels, *volume_size],
          device=device,
          dtype=torch.float32,
      )
      densities = torch.randn(
          size=[num_volumes, 1, *volume_size],
          device=device,
          dtype=torch.float32,
      )
  
      features_list, rand_sizes = TestVolumes._random_volume_list(
          num_volumes, 3, volume_size, num_channels, device
      )
      densities_list, _ = TestVolumes._random_volume_list(
          num_volumes, 3, volume_size, 1, device, rand_sizes=rand_sizes
      )
  
      volume_translation = -torch.randn(num_volumes, 3).type_as(
          features
      )
      voxel_size = torch.rand(num_volumes, 1).type_as(features) + 0.5
  
      for features_, densities_ in zip(
          (None, features, features_list),
          (densities, densities, densities_list),
      ):
          # init the volume structure
          v = Volumes(
              features=features_,
              densities=densities_,
              volume_translation=volume_translation,
              voxel_size=voxel_size,
          )
  
          # int index
          index = 1
          v_selected = v[index]
          self.assertEqual(len(v_selected), 1)
          self._check_indexed_volumes(v, v_selected, [(0, 1)])
  
          # list index
          index = [1, 2]
          v_selected = v[index]
          self.assertEqual(len(v_selected), len(index))
          self._check_indexed_volumes(v, v_selected, enumerate(index))
  
          # slice index
          index = slice(0, 2, 1)
          v_selected = v[0:2]
          self.assertEqual(len(v_selected), 2)
          self._check_indexed_volumes(v, v_selected, [(0, 0), (1, 1)])
  
          # bool tensor
          index = (torch.rand(num_volumes) > 0.5).to(device)
          index[:2] = True  # make sure smth is selected
          v_selected = v[index]
          self.assertEqual(len(v_selected), index.sum())
          self._check_indexed_volumes(
              v,
              v_selected,
              zip(
                  torch.arange(index.sum()),
                  torch.nonzero(index, as_tuple=False).squeeze(),
              ),
          )
  
          # int tensor
          index = torch.tensor([1, 2], dtype=torch.int64, device=device)
          v_selected = v[index]
          self.assertEqual(len(v_selected), index.numel())
          self._check_indexed_volumes(
              v, v_selected, enumerate(index.tolist())
          )
  
          # invalid index
          index = torch.tensor(
              [1, 0, 1], dtype=torch.float32, device=device
          )
          with self.assertRaises(IndexError):
              v_selected = v[index]
          index = 1.2  # floating point index
          with self.assertRaises(IndexError):
              v_selected = v[index]
  
imports: import copy
  import itertools
  import random
  import unittest
  import numpy
  import torch
  from pytorch3d.structures.volumes import VolumeLocator, Volumes
  from pytorch3d.transforms import Scale
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 4 for calling the TestVolumes obj's underlying member method code with example usage: obj.test_get_item(num_volumes, num_channels, volume_size, dtype) without return types