code: def test_dataset(self, value):
      if isinstance(value, list):
          self._test_dataset = value
      else:
          self._test_dataset = [value]
  
      for i, dataset in enumerate(self._test_dataset):
          if not hasattr(dataset, "name"):
              if self.num_test_datasets > 1:
                  setattr(dataset, "name", "test_%i" % i)
              else:
                  setattr(dataset, "name", "test")
          else:
              self._contains_dataset_name = True
  
      # Check for uniqueness
      all_names = [d.name for d in self.test_dataset]
      if len(set(all_names)) != len(all_names):
          raise ValueError(
              "Datasets need to have unique names. Current names are {}".format(
                  all_names
              )
          )
  
imports: import os
  from abc import ABC, abstractmethod
  import logging
  import functools
  from functools import partial
  import numpy
  import torch
  import torch_geometric
  from torch_geometric.transforms import Compose, FixedPoints
  import copy
  from torch_points3d.models import model_interface
  from torch_points3d.core.data_transform import instantiate_transforms, MultiScaleTransform
  from torch_points3d.core.data_transform import instantiate_filters
  from torch_points3d.datasets.batch import SimpleBatch
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.config import ConvolutionFormatFactory
  from torch_points3d.utils.colors import COLORS, colored_print
label_desc: Use image node_id 19 for calling the BaseDataset obj's underlying member method code with example usage: obj.test_dataset(value) without return types