docstr: Tests constructor for padded/list representations.
docstr_tok: ["Tests","constructor","for","padded\/list","representations","."]
code: def test_constructor_for_padded_lists(self):
      """
      Tests constructor for padded/list representations.
      """
  
      device = torch.device("cuda:0")
      diff_device = torch.device("cpu")
  
      num_volumes = 3
      num_channels = 4
      size = (6, 8, 10)
      diff_size = (6, 8, 11)
  
      # good ways to define densities
      ok_densities = [
          torch.randn(
              size=[num_volumes, 1, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(0),
          torch.randn(
              size=[num_volumes, 1, *size],
              device=device,
              dtype=torch.float32,
          ),
      ]
  
      # bad ways to define features
      bad_features = [
          torch.randn(
              size=[num_volumes + 1, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list with diff batch size
          torch.randn(
              size=[num_volumes + 1, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ),  # diff batch size
          torch.randn(
              size=[num_volumes, num_channels, *diff_size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list with different size
          torch.randn(
              size=[num_volumes, num_channels, *diff_size],
              device=device,
              dtype=torch.float32,
          ),  # different size
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=diff_device,
              dtype=torch.float32,
          ),  # different device
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=diff_device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list with different device
      ]
  
      # good ways to define features
      ok_features = [
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list of features of correct size
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ),
      ]
  
      for densities in ok_densities:
          for features in bad_features:
              self.assertRaises(
                  ValueError,
                  Volumes,
                  densities=densities,
                  features=features,
              )
          for features in ok_features:
              Volumes(densities=densities, features=features)
  
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 15 for calling the TestVolumes obj's underlying member method code with example usage: obj.test_constructor_for_padded_lists() without return types