code: def test_batchgraphdata_numpy_to_torch(self):
      import torch
  
      num_nodes_list, num_edge_list = [3, 4, 5], [2, 4, 5]
      num_node_features, num_edge_features = 32, 32
      edge_index_list = [
          np.array([[0, 1], [1, 2]]),
          np.array([[0, 1, 2, 3], [1, 2, 0, 2]]),
          np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]]),
      ]
  
      graph_list = [
          GraphData(
              node_features=np.random.random_sample(
                  (num_nodes_list[i], num_node_features)
              ),
              edge_index=edge_index_list[i],
              edge_features=np.random.random_sample(
                  (num_edge_list[i], num_edge_features)
              ),
              node_pos_features=None,
          )
          for i in range(len(num_edge_list))
      ]
      batched_graph = BatchGraphData(graph_list)
  
      batched_graph = batched_graph.numpy_to_torch()
  
      assert isinstance(batched_graph, BatchGraphData)
      assert isinstance(batched_graph.node_features, torch.Tensor)
      assert isinstance(batched_graph.edge_index, torch.Tensor)
      assert isinstance(batched_graph.edge_features, torch.Tensor)
      assert batched_graph.node_pos_features is None
  
file_path: deepchem/deepchem/feat/tests/test_graph_data.py
imports: import unittest
  import numpy
  import pytest
  from deepchem.feat.graph_data import BatchGraphData, GraphData, shortest_path_length
label_desc: Use image node_id 6 for calling the TestGraph obj's underlying member method code with example usage: obj.test_batchgraphdata_numpy_to_torch() without return types