feat: BucketResize node (cover-crop onto Klein buckets)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/test_bucket_node.py

@@ -0,0 +1,31 @@
+import torch
+from gates import bucket_node as bn
+
+
+def test_square_to_1280():
+    out, m, w, h, label = bn.BucketResize().run(image=torch.rand((1, 1000, 1000, 3)))
+    assert (w, h) == (1280, 1280)
+    assert out.shape == (1, 1280, 1280, 3)
+    assert m.shape == (1, 1280, 1280) and float(m.max()) == 0.0   # no mask -> zeros
+    assert label == "1280x1280"
+
+
+def test_landscape_bucket_shapes():
+    # tensor [B,H,W,3] with H=1000,W=2000 -> aspect 2.0 -> 1792x896
+    out, m, w, h, label = bn.BucketResize().run(image=torch.rand((1, 1000, 2000, 3)))
+    assert (w, h) == (1792, 896)
+    assert out.shape == (1, 896, 1792, 3)
+    assert label == "1792x896"
+
+
+def test_mask_resized_and_aligned():
+    out, m, w, h, _ = bn.BucketResize().run(
+        image=torch.rand((1, 1000, 1000, 3)), mask=torch.ones((1, 1000, 1000)))
+    assert m.shape == (1, 1280, 1280) and float(m.min()) > 0.9
+
+
+def test_outputs_are_on_grid():
+    out, m, w, h, _ = bn.BucketResize().run(
+        image=torch.rand((1, 777, 1333, 3)), resolution=1280, divisible=64)
+    assert w % 64 == 0 and h % 64 == 0
+    assert out.shape[1] == h and out.shape[2] == w