Format code with Black

Applied Black formatting to ensure compliance with code style guidelines.

Author: patricknihranz

inference/core/workflows/core_steps/fusion/buffer/v1.py

@@ -94,7 +94,11 @@ class BlockManifest(WorkflowBlockManifest):
         kind=[WILDCARD_KIND, LIST_OF_VALUES_KIND, IMAGE_KIND],
     ) = Field(
         description="Input data of any type to add to the buffer. Can be images, detections, values, or any other workflow output. Newest values are added to the beginning of the buffer array. The buffer maintains a sliding window of the most recent values.",
-        examples=["$steps.visualization", "$steps.object_detection_model.predictions", "$steps.image"],
+        examples=[
+            "$steps.visualization",
+            "$steps.object_detection_model.predictions",
+            "$steps.image",
+        ],
     )
     length: int = Field(
         description="Maximum number of elements to keep in the buffer. When the buffer exceeds this length, the oldest elements are automatically removed. Determines the size of the sliding window. Must be greater than 0. Typical values range from 2-10 for frame sequences, or higher for longer histories.",

inference/core/workflows/core_steps/fusion/detections_classes_replacement/v1.py

@@ -132,12 +132,18 @@ class BlockManifest(WorkflowBlockManifest):
     ) = Field(
         title="Regions of Interest",
         description="Detection predictions (object detection, instance segmentation, or keypoint detection) containing bounding boxes with generic class labels that will be replaced with classification results. These detections should correspond to the regions that were cropped and classified. Detections must have detection IDs that match the PARENT_ID_KEY in classification predictions. Detections at dimensionality level 1.",
-        examples=["$steps.object_detection_model.predictions", "$steps.instance_segmentation_model.predictions"],
+        examples=[
+            "$steps.object_detection_model.predictions",
+            "$steps.instance_segmentation_model.predictions",
+        ],
     )
     classification_predictions: Selector(kind=[CLASSIFICATION_PREDICTION_KIND]) = Field(
         title="Classification results for crops",
         description="Classification predictions from a classifier applied to cropped regions of the detections. Each classification result must have PARENT_ID_KEY (detection_id) linking it to its source detection. Supports both single-label (uses 'top' class) and multi-label (uses most confident class) classifications. Classification results at dimensionality level 2 (one classification per crop/detection).",
-        examples=["$steps.classification_model.predictions", "$steps.breed_classifier.predictions"],
+        examples=[
+            "$steps.classification_model.predictions",
+            "$steps.breed_classifier.predictions",
+        ],
     )
     fallback_class_name: Union[Optional[str], Selector(kind=[STRING_KIND])] = Field(
         default=None,

inference/core/workflows/core_steps/fusion/detections_stitch/v1.py

@@ -127,7 +127,11 @@ class BlockManifest(WorkflowBlockManifest):
         ]
     ) = Field(
         description="Model predictions (object detection or instance segmentation) from detection models that processed image slices or crops. These predictions must contain parent coordinate metadata indicating the position of each slice/crop in the original image. Predictions are collected from multiple slices/crops and merged into a single unified detection result. The block converts coordinates from slice/crop space to original image space and combines all detections.",
-        examples=["$steps.object_detection.predictions", "$steps.instance_segmentation.predictions", "$steps.slice_model.predictions"],
+        examples=[
+            "$steps.object_detection.predictions",
+            "$steps.instance_segmentation.predictions",
+            "$steps.slice_model.predictions",
+        ],
     )
     overlap_filtering_strategy: Union[
         Literal["none", "nms", "nmm"],

inference/core/workflows/core_steps/fusion/dimension_collapse/v1.py

@@ -89,7 +89,11 @@ class BlockManifest(WorkflowBlockManifest):
     type: Literal["roboflow_core/dimension_collapse@v1", "DimensionCollapse"]
     data: Selector() = Field(
         description="Reference to step outputs at dimensionality level n (nested batch structure) to be flattened and collapsed to level n-1. The input should be a nested batch (e.g., list of lists) where each nested level represents a batch dimension. The block flattens this structure by concatenating all nested elements into a single flat list. Common use cases: classification results from cropped images (level 2 → level 1), OCR results from cropped regions (level 2 → level 1), or any nested batch structure that needs to be flattened.",
-        examples=["$steps.classification_step.predictions", "$steps.ocr_step.results", "$steps.crop_classification.predictions"],
+        examples=[
+            "$steps.classification_step.predictions",
+            "$steps.ocr_step.results",
+            "$steps.crop_classification.predictions",
+        ],
     )
 
     @classmethod