class_docstr: TransformTest
      
code: def test_to_dense_batch(self):
      data1 = paddle.to_tensor(
          np.array([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype="float32")
      )
      data2 = paddle.to_tensor(
          np.array([[1, 2, 3], [3, 2, 1]], dtype="float32")
      )
      data3 = paddle.to_tensor(np.array([[1, 2, 3]], dtype="float32"))
      data = paddle.concat([data1, data2, data3], axis=0)
      common_edges = [(0, 0)]
      graph = pgl.Graph.disjoint(
          [
              pgl.Graph(num_nodes=i, edges=common_edges)
              for i in [3, 2, 1]
          ],
          merged_graph_index=False,
      ).tensor()
      out, mask = to_dense_batch(data, graph, fill_value=0)
      _out = [
          [[1, 2, 3], [3, 2, 1], [4, 5, 6]],
          [[1, 2, 3], [3, 2, 1], [0, 0, 0]],
          [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      ]
      self.assertAlmostEqual(out.numpy().tolist(), _out)
      _mask = [
          [False, False, False],
          [False, False, True],
          [False, True, True],
      ]
      self.assertAlmostEqual(mask.numpy().tolist(), _mask)
  
file_path: PGL/tests/test_transform.py
imports: import os
  import sys
  import re
  import time
  import unittest
  import numpy
  import paddle
  import pgl
  from pgl.utils.logger import log
  from pgl.utils.transform import to_dense_batch, filter_adj
label_desc: Use image node_id 1 for calling the TransformTest obj's underlying member method code with example usage: obj.test_to_dense_batch() without return types