diff --git a/lib/Conversion/TorchToLinalg/DataMovement.cpp b/lib/Conversion/TorchToLinalg/DataMovement.cpp
index 1cbb4aa99288..f3858034023c 100644
--- a/lib/Conversion/TorchToLinalg/DataMovement.cpp
+++ b/lib/Conversion/TorchToLinalg/DataMovement.cpp
@@ -1740,6 +1740,12 @@ class ConvertAtenTransposeIntOp
     Value outVector = tensor::EmptyOp::create(
         rewriter, loc, getAsOpFoldResult(outputDims), elementType);
 
+    // Note: The empty tensor type may not match `outType` due to folding
+    // performed by `getAsOpFoldResult` of `tensor::DimOp`.
+    // Cast to `outType` if needed to ensure type consistency.
+    if (outVector.getType() != outType)
+      outVector = tensor::CastOp::create(rewriter, loc, outType, outVector);
+
     SmallVector<int64_t> permutation(inputRank);
     std::iota(permutation.begin(), permutation.end(), 0);
     permutation[dim0] = dim1;
diff --git a/test/Conversion/TorchToLinalg/datamovement.mlir b/test/Conversion/TorchToLinalg/datamovement.mlir
index b24bd7bd2d4f..82ecd69dd996 100644
--- a/test/Conversion/TorchToLinalg/datamovement.mlir
+++ b/test/Conversion/TorchToLinalg/datamovement.mlir
@@ -71,3 +71,28 @@ func.func @torch.aten.reflection_pad2d(%arg0: !torch.vtensor<[1,1,4,4],f32>) ->
   %1 = torch.aten.reflection_pad2d %arg0, %0 : !torch.vtensor<[1,1,4,4],f32>, !torch.list<int> -> !torch.vtensor<[1,1,8,9],f32>
   return %1 : !torch.vtensor<[1,1,8,9],f32>
 }
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.transpose.int$dynamic_dims(
+// CHECK-SAME:                                                      %[[VAL_0:.*]]: !torch.vtensor<[1,56,56,96],f32>) -> !torch.vtensor<[?,?,?,?,?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[1,56,56,96],f32> -> tensor<1x56x56x96xf32>
+// CHECK:           %[[VAL_9:.*]] = tensor.expand_shape %[[VAL_1]] {{\[\[}}0], [1, 2], [3, 4], [5]] output_shape [1, 8, 7, 8, 7, 96] : tensor<1x56x56x96xf32> into tensor<1x8x7x8x7x96xf32>
+// CHECK:           %[[EMPTY:.*]] = tensor.empty() : tensor<1x8x8x7x7x96xf32>
+// CHECK:           %[[TRANSPOSE:.*]] = linalg.transpose ins(%[[VAL_9]] {{.*}} outs(%[[EMPTY]] {{.*}} permutation = [0, 1, 3, 2, 4, 5]
+// CHECK:           %[[RESULT_CAST:.*]] = tensor.cast %[[TRANSPOSE]] : tensor<1x8x8x7x7x96xf32> to tensor<?x?x?x?x?x?xf32>
+// CHECK:           %[[VAL_10:.*]] = torch_c.from_builtin_tensor %[[RESULT_CAST]] : tensor<?x?x?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?,?,?],f32>
+// CHECK:           return %[[VAL_10]] : !torch.vtensor<[?,?,?,?,?,?],f32>
+// CHECK:         }
+func.func @torch.aten.transpose.int$dynamic_dims(%arg0: !torch.vtensor<[1,56,56,96],f32>) -> !torch.vtensor<[?,?,?,?,?,?],f32> {
+  %int1 = torch.constant.int 1
+  %int8 = torch.constant.int 8
+  %int7 = torch.constant.int 7
+  %int96 = torch.constant.int 96
+  %int2 = torch.constant.int 2
+  %int3 = torch.constant.int 3
+  %0 = torch.prim.ListConstruct %int1, %int8, %int7, %int8, %int7, %int96 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  %1 = torch.aten.view %arg0, %0 : !torch.vtensor<[1,56,56,96],f32>, !torch.list<int> -> !torch.vtensor<[?,?,?,?,?,?],f32>
+  %2 = torch.aten.transpose.int %1, %int2, %int3 : !torch.vtensor<[?,?,?,?,?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?,?,?,?,?],f32>
+  return %2 : !torch.vtensor<[?,?,?,?,?,?],f32>
+}