code: def test_merge_preds(self):
      data_sample1 = TextRecogDataSample(
          pred_text=LabelData(
              score=torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]),
              text="abcde",
          )
      )
      data_sample2 = TextRecogDataSample(
          pred_text=LabelData(
              score=torch.tensor([0.2, 0.3, 0.4, 0.5, 0.6]),
              text="bcdef",
          )
      )
      data_sample3 = TextRecogDataSample(
          pred_text=LabelData(
              score=torch.tensor([0.3, 0.4, 0.5, 0.6, 0.7]),
              text="cdefg",
          )
      )
      aug_data_samples = [data_sample1, data_sample2, data_sample3]
      batch_aug_data_samples = [aug_data_samples] * 3
      model = EncoderDecoderRecognizerTTAModel(module=DummyModel())
      preds = model.merge_preds(batch_aug_data_samples)
      for pred in preds:
          self.assertEqual(pred.pred_text.text, "cdefg")
  
file_path: mmocr/tests/test_models/test_textrecog/test_recognizers/test_encoder_decoder_recognizer_tta.py
imports: from unittest import TestCase
  import torch
  import torch.nn
  from mmengine.structures import LabelData
  from mmocr.models.textrecog.recognizers import EncoderDecoderRecognizerTTAModel
  from mmocr.structures import TextRecogDataSample
label_desc: Use image node_id 1 for calling the TestEncoderDecoderRecognizerTTAModel obj's underlying member method code with example usage: obj.test_merge_preds() without return types