diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 66729d4445142..93622529a5d33 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -2749,8 +2749,69 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
               binder.op, "unimplemented: cubic coeff must be -0.75");
         }
 
+        Value inputTensor = operands[0];
+        Torch::ValueTensorType inputTensor_type =
+            cast<Torch::ValueTensorType>(inputTensor.getType());
+        ArrayRef<int64_t> inputTensor_sizes = inputTensor_type.getSizes();
+        ArrayRef<int64_t> outputTensor_sizes = outputTensor_type.getSizes();
+
+        int64_t const batchDimension = 0;
+        int64_t const channelDimension = 1;
+        int64_t nonScalableDimensions[] = {
+            batchDimension,
+            channelDimension,
+        };
+
+        auto errorMessageForScaling = [](int64_t givenDimension) {
+          switch (givenDimension) {
+          case batchDimension:
+            return "Unexpected intent to scale the batch dimension";
+          case channelDimension:
+            return "Unexpected intent to scale the channel dimension";
+          default:
+            return "Scalable dimension treated as non-scalable";
+          }
+        };
+
+        auto unknownSize = Torch::kUnknownSize;
+
+        // Compile-time check for dimensions of static size
+        for (auto eachDimension : nonScalableDimensions) {
+          auto eachInputSize = inputTensor_sizes[eachDimension];
+          auto eachOutputSize = outputTensor_sizes[eachDimension];
+
+          if (eachInputSize == unknownSize || eachOutputSize == unknownSize) {
+            continue;
+          } else if (eachInputSize == eachOutputSize) {
+            continue;
+          }
+
+          return rewriter.notifyMatchFailure(
+              binder.op, errorMessageForScaling(eachDimension));
+        }
+
         auto binderLocation = binder.getLoc();
 
+        // Run-time check for dimensions of dynamic size
+        for (auto eachDimension : nonScalableDimensions) {
+          auto eachDimensionAsValue = rewriter.create<Torch::ConstantIntOp>(
+              binderLocation, rewriter.getI64IntegerAttr(eachDimension));
+
+          Value eachInputSizeAsValue = rewriter.create<Torch::AtenSizeIntOp>(
+              binderLocation, inputTensor, eachDimensionAsValue);
+
+          int64_t eachOutputSize = outputTensor_sizes[eachDimension];
+          Value eachOutputSizeAsValue = rewriter.create<Torch::ConstantIntOp>(
+              binderLocation, rewriter.getI64IntegerAttr(eachOutputSize));
+
+          Value eachSizeComparison = rewriter.create<Torch::AtenEqIntOp>(
+              binderLocation, eachInputSizeAsValue, eachOutputSizeAsValue);
+
+          rewriter.create<Torch::RuntimeAssertOp>(
+              binderLocation, eachSizeComparison,
+              rewriter.getStringAttr(errorMessageForScaling(eachDimension)));
+        };
+
         Value cstFalse =
             rewriter.create<Torch::ConstantBoolOp>(binderLocation, false);
         Value cstTrue =
@@ -2770,10 +2831,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
               rewriter.create<Torch::ConstantStrOp>(binderLocation, modeStr);
         }
 
-        Value inputTensor = operands[0];
-        Torch::ValueTensorType inputTensor_type =
-            cast<Torch::ValueTensorType>(inputTensor.getType());
-        ArrayRef<int64_t> inputTensor_sizes = inputTensor_type.getSizes();
         unsigned inputTensor_rank = inputTensor_sizes.size();
 
         // supported modes:
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index 16c86218dbc8b..b4803be6ed3b9 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -2256,7 +2256,7 @@ func.func @test_sce_mean_3d_log_prob(%arg0: !torch.vtensor<[3,5,2],f32>, %arg1:
 // CHECK-LABEL: func.func @test_resize_sizes_nearest
   func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
     %none = torch.constant.none
-    // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
+    // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %8, %none_1, %str, %false, %none_1, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
     %0 = torch.operator "onnx.Resize"(%arg0, %none, %none, %arg1) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32>
     return %0 : !torch.vtensor<[?,?,?,?],f32>
   }
@@ -2267,7 +2267,7 @@ func.func @test_sce_mean_3d_log_prob(%arg0: !torch.vtensor<[3,5,2],f32>, %arg1:
 func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   %none = torch.constant.none
   // CHECK: %[[STR:.+]] = torch.constant.str "nearest_half_pixel,round_prefer_floor"
-  // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %[[STR]], %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
+  // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %8, %none_1, %[[STR]], %false, %none_1, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
   %0 = torch.operator "onnx.Resize"(%arg0, %none, %none, %arg1) {
     torch.onnx.coordinate_transformation_mode = "half_pixel",
     torch.onnx.mode = "nearest"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32>
@@ -2280,7 +2280,7 @@ func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1
   func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],
 f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
     %none = torch.constant.none
-    // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
+    // CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %8, %none_1, %str, %false, %none_1, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
     %0 = torch.operator "onnx.Resize"(%arg0, %none, %none, %arg1) {torch.onnx.mode = "linear"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32>
     return %0 : !torch.vtensor<[?,?,?,?],f32>
   }