auto updates (#7439)

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

Author: monai-bot

monai/apps/datasets.py

@@ -737,6 +737,7 @@ def get_dataset(self, folds: Sequence[int] | int, **dataset_params: Any) -> obje
         dataset_params_.update(dataset_params)
 
         class _NsplitsDataset(self.dataset_cls):  # type: ignore
+
             def _split_datalist(self, datalist: list[dict]) -> list[dict]:
                 data = partition_dataset(data=datalist, num_partitions=nfolds, shuffle=True, seed=seed)
                 return select_cross_validation_folds(partitions=data, folds=folds)

monai/apps/deepedit/transforms.py

@@ -34,6 +34,7 @@
 
 
 class DiscardAddGuidanced(MapTransform):
+
     def __init__(
         self,
         keys: KeysCollection,
@@ -84,6 +85,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
 
 
 class NormalizeLabelsInDatasetd(MapTransform):
+
     def __init__(
         self, keys: KeysCollection, label_names: dict[str, int] | None = None, allow_missing_keys: bool = False
     ):
@@ -121,6 +123,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
 
 
 class SingleLabelSelectiond(MapTransform):
+
     def __init__(
         self, keys: KeysCollection, label_names: Sequence[str] | None = None, allow_missing_keys: bool = False
     ):

monai/apps/detection/metrics/coco.py

@@ -72,6 +72,7 @@
 
 
 class COCOMetric:
+
     def __init__(
         self,
         classes: Sequence[str],

monai/apps/detection/utils/ATSS_matcher.py

@@ -164,6 +164,7 @@ def compute_matches(
 
 
 class ATSSMatcher(Matcher):
+
     def __init__(
         self,
         num_candidates: int = 4,

monai/apps/pathology/transforms/post/array.py

@@ -162,7 +162,7 @@ def __call__(self, prob_map: NdarrayOrTensor) -> NdarrayOrTensor:
         pred = label(pred)[0]
         if self.remove_small_objects is not None:
             pred = self.remove_small_objects(pred)
-        pred[pred > 0] = 1  # type: ignore
+        pred[pred > 0] = 1
 
         return convert_to_dst_type(pred, prob_map, dtype=self.dtype)[0]
 
@@ -338,7 +338,7 @@ def __call__(self, mask: NdarrayOrTensor, instance_border: NdarrayOrTensor) -> N
         instance_border = instance_border >= self.threshold  # uncertain area
 
         marker = mask - convert_to_dst_type(instance_border, mask)[0]  # certain foreground
-        marker[marker < 0] = 0  # type: ignore
+        marker[marker < 0] = 0
         marker = self.postprocess_fn(marker)
         marker = convert_to_numpy(marker)
 
@@ -634,7 +634,7 @@ def __call__(  # type: ignore
 
         seg_map_crop = convert_to_dst_type(seg_map_crop == instance_id, type_map_crop, dtype=bool)[0]
 
-        inst_type = type_map_crop[seg_map_crop]  # type: ignore
+        inst_type = type_map_crop[seg_map_crop]
         type_list, type_pixels = unique(inst_type, return_counts=True)
         type_list = list(zip(type_list, type_pixels))
         type_list = sorted(type_list, key=lambda x: x[1], reverse=True)

monai/data/dataset.py

@@ -1275,6 +1275,7 @@ def __len__(self) -> int:
         return min(len(dataset) for dataset in self.data)
 
     def _transform(self, index: int):
+
         def to_list(x):
             return list(x) if isinstance(x, (tuple, list)) else [x]
 

monai/fl/client/client_algo.py

@@ -57,6 +57,7 @@ def abort(self, extra: dict | None = None) -> None:
 
 
 class ClientAlgoStats(BaseClient):
+
     def get_data_stats(self, extra: dict | None = None) -> ExchangeObject:
         """
         Get summary statistics about the local data.

monai/handlers/ignite_metric.py

@@ -157,6 +157,7 @@ def attach(self, engine: Engine, name: str) -> None:  # type: ignore[override]
 
 @deprecated(since="1.2", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.")
 class IgniteMetric(IgniteMetricHandler):
+
     def __init__(
         self,
         metric_fn: CumulativeIterationMetric | None = None,

monai/metrics/f_beta_score.py

@@ -22,6 +22,7 @@
 
 
 class FBetaScore(CumulativeIterationMetric):
+
     def __init__(
         self,
         beta: float = 1.0,

monai/networks/blocks/dynunet_block.py

@@ -245,6 +245,7 @@ def forward(self, inp, skip):
 
 
 class UnetOutBlock(nn.Module):
+
     def __init__(
         self, spatial_dims: int, in_channels: int, out_channels: int, dropout: tuple | str | float | None = None
     ):