style(auto3dseg): format analyzer with black

Signed-off-by: Jun Hyeok Lee <bluehyena123@naver.com>

Author: bluehyena

monai/auto3dseg/analyzer.py

@@ -283,7 +283,9 @@ def __call__(self, data):
             a * b for a, b in zip(report[ImageStatsKeys.SHAPE][0], report[ImageStatsKeys.SPACING])
         ]
 
-        report[ImageStatsKeys.INTENSITY] = [self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_c) for nda_c in nda_croppeds]
+        report[ImageStatsKeys.INTENSITY] = [
+            self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_c) for nda_c in nda_croppeds
+        ]
 
         if not verify_report_format(report, self.get_report_format()):
             raise RuntimeError(f"report generated by {self.__class__} differs from the report format.")
@@ -358,7 +360,9 @@ def __call__(self, data: Mapping) -> dict:
 
         report = deepcopy(self.get_report_format())
 
-        report[ImageStatsKeys.INTENSITY] = [self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_f) for nda_f in nda_foregrounds]
+        report[ImageStatsKeys.INTENSITY] = [
+            self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_f) for nda_f in nda_foregrounds
+        ]
 
         if not verify_report_format(report, self.get_report_format()):
             raise RuntimeError(f"report generated by {self.__class__} differs from the report format.")
@@ -474,7 +478,9 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
             isinstance(t, (torch.Tensor, MetaTensor)) and t.device.type == "cuda" for t in (image_tensor, label_tensor)
         )
 
-        if isinstance(image_tensor, (MetaTensor, torch.Tensor)) and isinstance(label_tensor, (MetaTensor, torch.Tensor)):
+        if isinstance(image_tensor, (MetaTensor, torch.Tensor)) and isinstance(
+            label_tensor, (MetaTensor, torch.Tensor)
+        ):
             if label_tensor.device != image_tensor.device:
                 if using_cuda:
                     cuda_device = image_tensor.device if image_tensor.device.type == "cuda" else label_tensor.device
@@ -507,7 +513,9 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
             mask_index = ndas_label == index
 
             nda_masks = [nda[mask_index] for nda in ndas]
-            label_dict[LabelStatsKeys.IMAGE_INTST] = [self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_m) for nda_m in nda_masks]
+            label_dict[LabelStatsKeys.IMAGE_INTST] = [
+                self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_m) for nda_m in nda_masks
+            ]
 
             pixel_count = sum(mask_index)
             pixel_arr.append(pixel_count)
@@ -530,7 +538,9 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
 
         report = deepcopy(self.get_report_format())
         report[LabelStatsKeys.LABEL_UID] = unique_label
-        report[LabelStatsKeys.IMAGE_INTST] = [self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_f) for nda_f in nda_foregrounds]
+        report[LabelStatsKeys.IMAGE_INTST] = [
+            self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_f) for nda_f in nda_foregrounds
+        ]
         report[LabelStatsKeys.LABEL] = label_substats
 
         if not verify_report_format(report, self.get_report_format()):