code: def setUp(self):
      # test decoding with text center region of small area
      maps = torch.zeros((1, 5, 224, 224), dtype=torch.float)
      maps[:, 0:2, :, :] = -10.0
      maps[:, 0, 60:100, 50:170] = 10.0
      maps[:, 1, 75:85, 60:160] = 10.0
      maps[:, 2, 75:85, 60:160] = 0.0
      maps[:, 3, 75:85, 60:160] = 1.0
      maps[:, 4, 75:85, 60:160] = 10.0
      maps[:, 0:2, 150:152, 5:7] = 10.0
      self.pred_result1 = copy.deepcopy(maps)
      # test decoding with small radius
      maps.fill_(0.0)
      maps[:, 0:2, :, :] = -10.0
      maps[:, 0, 120:140, 20:40] = 10.0
      maps[:, 1, 120:140, 20:40] = 10.0
      maps[:, 2, 120:140, 20:40] = 0.0
      maps[:, 3, 120:140, 20:40] = 1.0
      maps[:, 4, 120:140, 20:40] = 0.5
      self.pred_result2 = copy.deepcopy(maps)
  
      self.data_sample = TextDetDataSample(
          metainfo=dict(scale_factor=(0.5, 1)),
          gt_instances=InstanceData(
              polygons=[
                  np.array([0, 0, 0, 1, 2, 1, 2, 0]),
                  np.array([1, 1, 1, 2, 3, 2, 3, 1]),
              ]
          ),
      )
  
file_path: mmocr/tests/test_models/test_textdet/test_postprocessors/test_textsnake_postprocessor.py
imports: import copy
  import unittest
  import numpy
  import torch
  from mmengine.structures import InstanceData
  from parameterized import parameterized
  from mmocr.models.textdet.postprocessors import TextSnakePostprocessor
  from mmocr.structures import TextDetDataSample
label_desc: Use image node_id 1 for calling the TestTextSnakePostProcessor obj's underlying member method code with example usage: obj.setUp() without return types