code: def test_filter(self):
      pyg_dataset = TUDataset("./enzymes", "ENZYMES")
      ds = pyg_to_dicts(pyg_dataset)
      graphs = [Graph(**item) for item in ds]
      dataset = GraphDataset(graphs, task="graph")
      thresh = 90
  
      orig_dataset_size = len(dataset)
      num_graphs_large = 0
      for graph in dataset:
          if graph.num_nodes >= thresh:
              num_graphs_large += 1
  
      dataset = dataset.filter(
          lambda graph: graph.num_nodes < thresh, deep_copy=False
      )
      filtered_dataset_size = len(dataset)
  
      self.assertEqual(
          orig_dataset_size - filtered_dataset_size,
          num_graphs_large,
      )
  
imports: import copy
  import random
  import torch
  import unittest
  from torch_geometric.datasets import TUDataset, Planetoid
  from copy import deepcopy
  from deepsnap.graph import Graph
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset, Generator, EnsembleGenerator
  from tests.utils import pyg_to_dicts, simple_networkx_graph, simple_networkx_small_graph, simple_networkx_graph_alphabet, simple_networkx_multigraph, generate_dense_hete_dataset, generate_simple_small_hete_graph, gen_graph
label_desc: Use image node_id 6 for calling the TestDatasetTensorBackend obj's underlying member method code with example usage: obj.test_filter() without return types