code: def test_get_data_struct(landmark_uncertainty_tuples_path):
      (
          bins_all_targets,
          bins_targets_sep,
          bounds_all_targets,
          bounds_targets_sep,
      ) = generate_struct_for_qbin(
          ["U-NET"], [0, 1], landmark_uncertainty_tuples_path[2], "SA"
      )
  
      assert isinstance(bins_all_targets, dict)
      assert isinstance(bins_all_targets["U-NET"], pd.DataFrame)
      assert sorted(list(bins_all_targets["U-NET"].keys())) == sorted(
          EXPECTED_COLS
      )
      assert isinstance(bins_targets_sep, dict)
      assert list(bins_targets_sep.keys()) == ["U-NET L0", "U-NET L1"]
      assert sorted(
          list(bins_targets_sep["U-NET L0"].keys())
      ) == sorted(EXPECTED_COLS)
  
      assert isinstance(bounds_all_targets, dict)
      assert isinstance(bounds_targets_sep, dict)
  
file_path: pykale/tests/prepdata/test_tabular_transform.py
imports: import logging
  import numpy
  import pandas
  import pytest
  import torch
  from kale.prepdata.tabular_transform import apply_confidence_inversion, generate_struct_for_qbin, ToOneHotEncoding, ToTensor
  from kale.utils.seed import set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: test_get_data_struct(landmark_uncertainty_tuples_path) without return types