code: def test_pna_conv_get_degree_histogram_dataloader():
      edge_index_1 = torch.tensor(
          [[0, 0, 0, 1, 1, 2, 3], [1, 2, 3, 2, 0, 0, 0]]
      )
      edge_index_2 = torch.tensor(
          [[1, 1, 2, 2, 0, 3, 3], [2, 3, 3, 1, 1, 0, 2]]
      )
      edge_index_3 = torch.tensor(
          [[1, 3, 2, 0, 0, 4, 2], [2, 0, 4, 1, 1, 0, 3]]
      )
      edge_index_4 = torch.tensor(
          [[0, 1, 2, 4, 0, 1, 3], [2, 3, 3, 1, 1, 0, 2]]
      )
  
      data_1 = Data(
          num_nodes=5, edge_index=edge_index_1
      )  # hist = [1, 2 ,1 ,1]
      data_2 = Data(
          num_nodes=5, edge_index=edge_index_2
      )  # hist = [1, 1, 3]
      data_3 = Data(
          num_nodes=5, edge_index=edge_index_3
      )  # hist = [0, 3, 2]
      data_4 = Data(
          num_nodes=5, edge_index=edge_index_4
      )  # hist = [1, 1, 3]
  
      loader = DataLoader(
          [data_1, data_2, data_3, data_4],
          batch_size=1,
          shuffle=False,
      )
      deg_hist = PNAConv.get_degree_histogram(loader)
      assert torch.equal(deg_hist, torch.tensor([3, 7, 9, 1]))
  
file_path: pytorch_geometric/test/nn/conv/test_pna_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.data import Data
  from torch_geometric.loader import DataLoader, NeighborLoader
  from torch_geometric.nn import PNAConv
  from torch_geometric.testing import is_full_test, onlyNeighborSampler
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 3 for calling a global function with example usage: test_pna_conv_get_degree_histogram_dataloader() without return types