Formatting

Author: xenova

tests/utils/tensor.test.js

@@ -69,7 +69,11 @@ describe("Tensor operations", () => {
     });
 
     it("should return a crop", async () => {
-      const t1 = new Tensor("float32", Array.from({ length: 28 }, (_, i) => i + 1), [4, 7]);
+      const t1 = new Tensor(
+        "float32",
+        Array.from({ length: 28 }, (_, i) => i + 1),
+        [4, 7],
+      );
       const t2 = t1.slice([1, -1], [1, -1]);
 
       const target = new Tensor("float32", [9, 10, 11, 12, 13, 16, 17, 18, 19, 20], [2, 5]);

tests/utils/utils.test.js

@@ -65,14 +65,14 @@ describe("Utilities", () => {
     const [width, height, channels] = [2, 2, 3];
     const data = Uint8Array.from({ length: width * height * channels }, (_, i) => i % 5);
     const tiny_image = new RawImage(data, width, height, channels);
-    
+
     let image;
     beforeAll(async () => {
       image = await RawImage.fromURL("https://picsum.photos/300/200");
     });
 
     it("Can split image into separate channels", async () => {
-      const image_data = tiny_image.split().map(x => x.data);
+      const image_data = tiny_image.split().map((x) => x.data);
 
       const target = [
         new Uint8Array([0, 3, 1, 4]), // Reds
@@ -84,7 +84,10 @@ describe("Utilities", () => {
     });
 
     it("Can splits channels for grayscale", async () => {
-      const image_data = tiny_image.grayscale().split().map(x => x.data);
+      const image_data = tiny_image
+        .grayscale()
+        .split()
+        .map((x) => x.data);
       const target = [new Uint8Array([1, 3, 2, 1])];
 
       compare(image_data, target);