diff --git a/include/torch-mlir/Dialect/Torch/IR/TorchTypes.h b/include/torch-mlir/Dialect/Torch/IR/TorchTypes.h
index 163ed63008789..9235189fb0314 100644
--- a/include/torch-mlir/Dialect/Torch/IR/TorchTypes.h
+++ b/include/torch-mlir/Dialect/Torch/IR/TorchTypes.h
@@ -84,6 +84,10 @@ class BaseTensorType : public Type {
   /// Enable isa/dyn_cast for BaseTensorType.
   static bool classof(Type type);
 
+  /// The element-wise comparison of each dimension/size in `that` tensor
+  std::vector<std::optional<bool>>
+  shapeComparisonAgainst(BaseTensorType that) const;
+
   /// Return true if this type has the same sizes and dtype as the other.
   bool hasSameSizesAndDtype(BaseTensorType other) const;
 
diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index d82d9251029bc..8de0646b08482 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -2717,6 +2717,32 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                                           "round_prefer_floor") ||
             binder.f32FloatAttr(cubic_coeff_a, "cubic_coeff_a", -0.75))
           return failure();
+
+        Value inputTensor = operands[0];
+        Torch::ValueTensorType inputTensor_blueprint =
+            cast<Torch::ValueTensorType>(inputTensor.getType());
+
+        std::vector<std::optional<bool>> shapeComparison =
+            inputTensor_blueprint.shapeComparisonAgainst(
+                outputTensor_blueprint);
+
+        // Comparisons of the dimensions assumed to carry the batch and channel
+        auto shapeComparisonForFixedDimensions =
+            ArrayRef(shapeComparison).take_front(2);
+
+        for (auto eachDimensionComparison : shapeComparisonForFixedDimensions) {
+          if (eachDimensionComparison == std::nullopt) {
+            return rewriter.notifyMatchFailure(
+                binder.op, "Sizes for batch and channel dimensions must be "
+                           "statically defined");
+          }
+          if (eachDimensionComparison == false) {
+            return rewriter.notifyMatchFailure(
+                binder.op,
+                "Unexpected intent to resize the batch/channel dimensions");
+          }
+        };
+
         if (antialias != 0) {
           return rewriter.notifyMatchFailure(
               binder.op,
@@ -2749,9 +2775,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
               binder.op, "unimplemented: cubic coeff must be -0.75");
         }
 
-        Value inputTensor = operands[0];
-        Torch::ValueTensorType inputTensor_blueprint =
-            cast<Torch::ValueTensorType>(inputTensor.getType());
         ArrayRef<int64_t> inputTensor_dimensions =
             inputTensor_blueprint.getSizes();
         unsigned rank = inputTensor_dimensions.size();
diff --git a/lib/Dialect/Torch/IR/TorchTypes.cpp b/lib/Dialect/Torch/IR/TorchTypes.cpp
index c46865ee5fed5..dd55a29b73fd2 100644
--- a/lib/Dialect/Torch/IR/TorchTypes.cpp
+++ b/lib/Dialect/Torch/IR/TorchTypes.cpp
@@ -217,6 +217,30 @@ static bool isValidTorchDtype(Type dtype) {
   return false;
 }
 
+std::vector<std::optional<bool>>
+BaseTensorType::shapeComparisonAgainst(BaseTensorType that) const {
+  auto this_dimensions = /**/ getSizes();
+  auto that_dimensions = that.getSizes();
+
+  auto this_rank = this_dimensions.size();
+  auto that_rank = that_dimensions.size();
+
+  assert((this_rank == that_rank) && "Ranks must match to compare dimensions");
+
+  std::vector<std::optional<bool>> runningComparison = {};
+  auto dimensionPairs = llvm::zip(this_dimensions, that_dimensions);
+
+  for (auto [eachLHDimension, eachRHDimension] : dimensionPairs) {
+    if (eachLHDimension == kUnknownSize || eachRHDimension == kUnknownSize) {
+      runningComparison.push_back(std::nullopt);
+    } else {
+      runningComparison.push_back(eachLHDimension == eachRHDimension);
+    }
+  }
+
+  return runningComparison;
+}
+
 bool BaseTensorType::hasSameSizesAndDtype(BaseTensorType other) const {
   return getOptionalSizes() == other.getOptionalSizes() &&
          getOptionalDtype() == other.getOptionalDtype();
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index 16c86218dbc8b..ac1e82f0c6e6f 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -2254,35 +2254,35 @@ 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 = ""} {
+  func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[1,1,?,?],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>
-    %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>
+    // 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<[1,1,?,?],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<[1,1,?,?],f32>
+    return %0 : !torch.vtensor<[1,1,?,?],f32>
   }
 
 // -----
 
 // 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 = ""} {
+func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[1,1,?,?],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, %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<[1,1,?,?],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>
-  return %0 : !torch.vtensor<[?,?,?,?],f32>
+    torch.onnx.mode = "nearest"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[1,1,?,?],f32>
+  return %0 : !torch.vtensor<[1,1,?,?],f32>
 }
 
 // -----
 
 // CHECK-LABEL: func.func @test_resize_sizes_linear
-  func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],
+  func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[1,1,?,?],
 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>
-    %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>
+    // 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<[1,1,?,?],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<[1,1,?,?],f32>
+    return %0 : !torch.vtensor<[1,1,?,?],f32>
   }
 
 // -----