code: def test_convert_dict_to_df():
      d = {
          "binary_C82EB": {
              "predictions": torch.tensor([True, True, True, False]),
              "probabilities": torch.tensor(
                  [
                      [0.4777, 0.5223],
                      [0.4482, 0.5518],
                      [0.4380, 0.5620],
                      [0.5059, 0.4941],
                  ]
              ),
          },
          "category_1491D": {
              "predictions": ["NkNUG", "NkNUG", "NkNUG", "NkNUG"],
              "probabilities": torch.tensor(
                  [
                      [0.1058, 0.4366, 0.1939, 0.2637],
                      [0.0816, 0.4807, 0.1978, 0.2399],
                      [0.0907, 0.4957, 0.1829, 0.2308],
                      [0.0728, 0.5015, 0.1900, 0.2357],
                  ]
              ),
          },
          "num_7B25F": {
              "predictions": torch.tensor(
                  [2.0436, 2.1158, 2.1222, 2.1964]
              )
          },
      }
  
      df = convert_dict_to_df(d)
  
      assert df.shape == (4, 5)
      # Check that all elements in nested lists are stored in each row
      assert all(
          len(row) == 2 for row in df["binary_C82EB_probabilities"]
      )
      assert all(
          len(row) == 4 for row in df["category_1491D_probabilities"]
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_convert_dict_to_df() without return types