reroll modified tests

Signed-off-by: R. Garcia-Dias <[email protected]>

Author: garciadias

MONAI

@@ -0,0 +1 @@
+Subproject commit f97a0e9bc7787df261284db2cdeffa2e8d631512

monai/utils/jupyter_utils.py

@@ -234,7 +234,7 @@ def plot_engine_status(
 
 
 def _get_loss_from_output(
-    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor,
+    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor
 ) -> torch.Tensor:
     """Returns a single value from the network output, which is a dict or tensor."""
 

monai_src

@@ -0,0 +1 @@
+Subproject commit f97a0e9bc7787df261284db2cdeffa2e8d631512

tests/transforms/test_gibbs_noise.py

@@ -13,7 +13,6 @@
 
 import unittest
 from copy import deepcopy
-from itertools import product
 
 import numpy as np
 from parameterized import parameterized
@@ -22,13 +21,13 @@
 from monai.transforms import GibbsNoise
 from monai.utils.misc import set_determinism
 from monai.utils.module import optional_import
-from tests.test_utils import TEST_NDARRAYS, assert_allclose
+from tests.test_utils import TEST_NDARRAYS, assert_allclose, dict_product
 
 _, has_torch_fft = optional_import("torch.fft", name="fftshift")
 
 shapes = ((128, 64), (64, 48, 80))
 input_types = TEST_NDARRAYS if has_torch_fft else [np.array]
-TEST_CASES = list(map(list, product(shape=shapes, input_type=input_types)))
+TEST_CASES = [[p_dict["shape"], p_dict["input_type"]] for p_dict in dict_product(shape=shapes, input_type=input_types)]
 
 
 class TestGibbsNoise(unittest.TestCase):