Formatting.

Author: Josh Fromm

src/relax/op/image/resize.cc

@@ -123,7 +123,8 @@ InferLayoutOutput InferLayoutResize2d(const Call& call,
     data_layout = GetLayoutDecision(var_layout_map, call->args[0]);
     new_attrs->layout = TransposeLike(attrs->layout, InitialLayout(4), data_layout->layout).name();
   }
-  return InferLayoutOutput({data_layout, InitialNLayout(call->args[1])}, {data_layout}, Attrs(new_attrs));
+  return InferLayoutOutput({data_layout, InitialNLayout(call->args[1])}, {data_layout},
+                           Attrs(new_attrs));
 }
 
 TVM_REGISTER_OP("relax.image.resize2d")

tests/python/relax/test_frontend_nn_modules.py

@@ -257,11 +257,13 @@ def forward(
         R.func_attr({"num_input": 2})
         with R.dataflow():
             lv1: R.Tensor((1, 32, 30, 30, 30), dtype="float32") = R.nn.conv3d(x, weight)
-            lv2: R.Tensor((1, 32, 1, 1, 1), dtype="float32") = R.reshape(bias, R.shape([1, 32, 1, 1, 1]))
-            conv3d: R.Tensor((1, 32, 30, 30, 30), dtype="float32") = R.add(lv1, lv2)
-            gv1: R.Tuple(R.Tensor((1, 32, 30, 30, 30), dtype="float32"), R.Tuple(R.Object)) = conv3d, (
-                _io,
+            lv2: R.Tensor((1, 32, 1, 1, 1), dtype="float32") = R.reshape(
+                bias, R.shape([1, 32, 1, 1, 1])
             )
+            conv3d: R.Tensor((1, 32, 30, 30, 30), dtype="float32") = R.add(lv1, lv2)
+            gv1: R.Tuple(
+                R.Tensor((1, 32, 30, 30, 30), dtype="float32"), R.Tuple(R.Object)
+            ) = conv3d, (_io,)
             R.output(gv1)
         return gv1