code: def test_predictupsampledense(self):
      dataset = ForwardShapenetDataset(self.config)
      dataset.create_dataloaders(
          MockModel(DictConfig({"conv_type": "DENSE"})),
          2,
          False,
          1,
          False,
      )
      forward_set = dataset.test_dataloaders[0]
      for b in forward_set:
          output = torch.tensor([[1, 0], [1, 0], [0, 1], [0, 1]])
          predicted = dataset.predict_original_samples(
              b, "DENSE", output
          )
          self.assertEqual(len(predicted), 2)
          self.assertEqual(predicted["example1.txt"].shape, (3, 4))
          self.assertEqual(predicted["example2.txt"].shape, (4, 4))
          npt.assert_allclose(
              predicted["example1.txt"][:, -1], np.asarray([0, 0, 0])
          )
          npt.assert_allclose(
              predicted["example2.txt"][:, -1], np.asarray([1, 1, 1, 1])
          )
  
imports: import unittest
  import os
  import sys
  import torch
  from omegaconf.dictconfig import DictConfig
  import numpy.testing
  import numpy
  import copy
  from test.mock_models import MockModel
  from torch_points3d.datasets.segmentation.forward.shapenet import _ForwardShapenet, ForwardShapenetDataset
label_desc: Use image node_id 6 for calling the TestForwardData obj's underlying member method code with example usage: obj.test_predictupsampledense() without return types