diff --git a/Project.toml b/Project.toml
index 60b8c3c..321cdfb 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "BlockSparseArrays"
 uuid = "2c9a651f-6452-4ace-a6ac-809f4280fbb4"
 authors = ["ITensor developers <support@itensor.org> and contributors"]
-version = "0.10.14"
+version = "0.10.15"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -42,7 +42,7 @@ MapBroadcast = "0.1.5"
 MatrixAlgebraKit = "0.6"
 SparseArraysBase = "0.7.1"
 SplitApplyCombine = "1.2.3"
-TensorAlgebra = "0.6"
+TensorAlgebra = "0.6.2"
 Test = "1.10"
 TypeParameterAccessors = "0.4.1"
 julia = "1.10"
diff --git a/ext/BlockSparseArraysTensorAlgebraExt/BlockSparseArraysTensorAlgebraExt.jl b/ext/BlockSparseArraysTensorAlgebraExt/BlockSparseArraysTensorAlgebraExt.jl
index 4874dcd..3a71e90 100644
--- a/ext/BlockSparseArraysTensorAlgebraExt/BlockSparseArraysTensorAlgebraExt.jl
+++ b/ext/BlockSparseArraysTensorAlgebraExt/BlockSparseArraysTensorAlgebraExt.jl
@@ -8,13 +8,27 @@ using TensorAlgebra: TensorAlgebra, BlockReshapeFusion, BlockedTuple, matricize,
     matricize_axes, tensor_product_axis, unmatricize
 
 function TensorAlgebra.tensor_product_axis(
-        ::BlockReshapeFusion, r1::BlockUnitRange, r2::BlockUnitRange
+        style::BlockReshapeFusion, side::Val{:codomain}, r1::BlockUnitRange, r2::BlockUnitRange
+    )
+    return tensor_product_blockrange(style, side, r1, r2)
+end
+function TensorAlgebra.tensor_product_axis(
+        style::BlockReshapeFusion, side::Val{:domain}, r1::BlockUnitRange, r2::BlockUnitRange
+    )
+    return tensor_product_blockrange(style, side, r1, r2)
+end
+function tensor_product_blockrange(
+        ::BlockReshapeFusion, side::Val, r1::BlockUnitRange, r2::BlockUnitRange
     )
     (isone(first(r1)) && isone(first(r2))) ||
         throw(ArgumentError("Only one-based axes are supported"))
     blockaxpairs = Iterators.product(eachblockaxes1(r1), eachblockaxes1(r2))
-    blockaxs = vec(splat(tensor_product_axis).(blockaxpairs))
-    return blockrange(blockaxs)
+    blockaxs = map(blockaxpairs) do (b1, b2)
+        # TODO: Store a FusionStyle for the blocks in `BlockReshapeFusion`
+        # and use that here.
+        return tensor_product_axis(side, b1, b2)
+    end
+    return blockrange(vec(blockaxs))
 end
 
 function TensorAlgebra.matricize(