diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
index 510ce8121..421b4edc9 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
@@ -426,6 +426,342 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
         }
         return failure();
       });
+  patterns.onOp(
+      "Conv", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        std::string autoPad;
+        if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
+          return failure();
+        if (autoPad != "NOTSET") {
+          // TODO: Add support for `auto_pad` != "NOTSET"
+          return rewriter.notifyMatchFailure(
+              binder.op, "unsupported conversion: auto_pad != NOTSET");
+        }
+
+        Torch::ValueTensorType resultType;
+        Value input, weight;
+        int64_t group;
+        if (binder.tensorOperands(input, weight) ||
+            binder.s64IntegerAttr(group, "group", 1) ||
+            binder.tensorResultType(resultType))
+          return failure();
+
+        auto weightTensorType = weight.getType().cast<Torch::ValueTensorType>();
+        if (!weightTensorType || !weightTensorType.hasSizes()) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "Expected weight type having sizes");
+        }
+        ArrayRef<int64_t> weightShape = weightTensorType.getSizes();
+        SmallVector<int64_t> kernelShape;
+        if (binder.s64IntegerArrayAttr(kernelShape, "kernel_shape", {}))
+          return failure();
+        if (kernelShape.size()) {
+          if (kernelShape.size() != weightShape.size() - 2) {
+            return rewriter.notifyMatchFailure(
+                binder.op,
+                "unsupported conversion: kernel_shape list size should have "
+                "number of values equal to weight_rank - 2");
+          } else {
+            for (unsigned i = 0; i < kernelShape.size(); i++) {
+              if (weightShape[i + 2] != kernelShape[i]) {
+                return rewriter.notifyMatchFailure(
+                    binder.op, "unsupported conversion: kernel_shape value "
+                               "should be equal to the weight tensor shape");
+              }
+            }
+          }
+        }
+
+        // Determine the rank of input tensor.
+        std::optional<unsigned> maybeRank = Torch::getTensorRank(input);
+        if (!maybeRank)
+          return rewriter.notifyMatchFailure(binder.op,
+                                             "Unimplemented: unranked tensor");
+        unsigned rank = *maybeRank;
+
+        SmallVector<int64_t> padding, strides, dilations;
+        SmallVector<int64_t> defaultPadding, defaultStrides, defaultDilations;
+        for (unsigned i = 0; i < rank - 2; i++) {
+          defaultPadding.push_back(0);
+          defaultStrides.push_back(1);
+          defaultDilations.push_back(1);
+        }
+        // Padding for the beginning and ending along each spatial axis, it can
+        // take any value greater than or equal to 0. The value represent the
+        // number of pixels added to the beginning and end part of the
+        // corresponding axis. pads format should be as follow [x1_begin,
+        // x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
+        // at the beginning of axis i and xi_end, the number of pixels added at
+        // the end of axis i.
+        if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding)) {
+          return failure();
+        }
+        if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "padding list size does not match the number of axes");
+        }
+        if (binder.s64IntegerArrayAttr(dilations, "dilations",
+                                       defaultDilations)) {
+          return failure();
+        }
+        if (dilations.size() != rank - 2) {
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "dilations list size does not match the number of axes");
+        }
+        if (binder.s64IntegerArrayAttr(strides, "strides", defaultStrides)) {
+          return failure();
+        }
+        if (strides.size() != rank - 2) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "strides list size does not match the number of axes");
+        }
+
+        SmallVector<Value> cstPadding, cstStrides, cstDilations,
+            cstOutputPadding;
+        if (padding.size() != 2 * (rank - 2)) {
+          for (int64_t i : padding) {
+            cstPadding.push_back(rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+          }
+        } else {
+          for (unsigned i = 0; i < padding.size() / 2; i++) {
+            if (padding[i] != padding[i + (padding.size() / 2)]) {
+              // TODO: Add support for different padding values for the
+              // beginning and ending along each spatial axis
+              return rewriter.notifyMatchFailure(
+                  binder.op,
+                  "unsupported conversion: padding values for the beginning "
+                  "and ending along each spatial axis must be equal");
+            }
+            cstPadding.push_back(rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getI64IntegerAttr(padding[i])));
+          }
+        }
+        for (int64_t i : dilations) {
+          cstDilations.push_back(rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+        }
+        for (int64_t i : strides) {
+          cstStrides.push_back(rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+        }
+        Value cstZero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(0));
+        cstOutputPadding = {cstZero, cstZero};
+
+        Value paddingList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstPadding);
+        Value dilationsList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstDilations);
+        Value stridesList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstStrides);
+        Value outputPaddingList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstOutputPadding);
+        Value transposed =
+            rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
+        Value bias;
+        if (binder.op->getNumOperands() == 3) {
+          if (binder.tensorOperandAtIndex(bias, 2)) {
+            return failure();
+          }
+        } else {
+          bias = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+        }
+        Value cstGroup = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(group));
+
+        rewriter.replaceOpWithNewOp<Torch::AtenConvolutionOp>(
+            binder.op, resultType, input, weight, bias, stridesList,
+            paddingList, dilationsList, transposed, outputPaddingList,
+            cstGroup);
+        return success();
+      });
+  patterns.onOp(
+      "ConvTranspose", 11,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        std::string autoPad;
+        if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
+          return failure();
+        if (autoPad != "NOTSET") {
+          // TODO: Add support for `auto_pad` != "NOTSET"
+          return rewriter.notifyMatchFailure(
+              binder.op, "unsupported conversion: auto_pad != NOTSET");
+        }
+        SmallVector<int64_t> outputShape;
+        if (binder.s64IntegerArrayAttr(outputShape, "output_shape", {}))
+          return failure();
+        if (outputShape.size()) {
+          // TODO: Add support for non-None output_shape value.
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "unsupported conversion: output_shape should be absent");
+        }
+        Torch::ValueTensorType resultType;
+        Value input, weight;
+        int64_t group;
+        if (binder.tensorOperands(input, weight) ||
+            binder.s64IntegerAttr(group, "group", 1) ||
+            binder.tensorResultType(resultType))
+          return failure();
+
+        auto weightTensorType = weight.getType().cast<Torch::ValueTensorType>();
+        if (!weightTensorType || !weightTensorType.hasSizes()) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "Expected weight type having sizes");
+        }
+        ArrayRef<int64_t> weightShape = weightTensorType.getSizes();
+        SmallVector<int64_t> kernelShape;
+        if (binder.s64IntegerArrayAttr(kernelShape, "kernel_shape", {}))
+          return failure();
+        if (kernelShape.size()) {
+          if (kernelShape.size() != weightShape.size() - 2) {
+            return rewriter.notifyMatchFailure(
+                binder.op,
+                "unsupported conversion: kernel_shape list size should have "
+                "number of values equal to weight_rank - 2");
+          } else {
+            for (unsigned i = 0; i < kernelShape.size(); i++) {
+              if (weightShape[i + 2] != kernelShape[i]) {
+                return rewriter.notifyMatchFailure(
+                    binder.op, "unsupported conversion: kernel_shape value "
+                               "should be equal to the weight tensor shape");
+              }
+            }
+          }
+        }
+
+        // Determine the rank of input tensor.
+        std::optional<unsigned> maybeRank = Torch::getTensorRank(input);
+        if (!maybeRank)
+          return rewriter.notifyMatchFailure(binder.op,
+                                             "Unimplemented: unranked tensor");
+        unsigned rank = *maybeRank;
+
+        SmallVector<int64_t> padding, strides, dilations, outputPadding;
+        SmallVector<int64_t> defaultPadding, defaultStrides, defaultDilations, defaultOutputPadding;
+        for (unsigned i = 0; i < rank - 2; i++) {
+          defaultPadding.push_back(0);
+          defaultStrides.push_back(1);
+          defaultDilations.push_back(1);
+          defaultOutputPadding.push_back(0);
+        }
+        // Padding for the beginning and ending along each spatial axis, it can
+        // take any value greater than or equal to 0. The value represent the
+        // number of pixels added to the beginning and end part of the
+        // corresponding axis. pads format should be as follow [x1_begin,
+        // x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
+        // at the beginning of axis i and xi_end, the number of pixels added at
+        // the end of axis i.
+        if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding)) {
+          return failure();
+        }
+        if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "padding list size does not match the number of axes");
+        }
+        if (binder.s64IntegerArrayAttr(dilations, "dilations",
+                                       defaultDilations)) {
+          return failure();
+        }
+        if (dilations.size() != rank - 2) {
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "dilations list size does not match the number of axes");
+        }
+        if (binder.s64IntegerArrayAttr(strides, "strides", defaultStrides)) {
+          return failure();
+        }
+        if (strides.size() != rank - 2) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "strides list size does not match the number of axes");
+        }
+        if (binder.s64IntegerArrayAttr(outputPadding, "output_padding",
+                                       defaultOutputPadding)) {
+          return failure();
+        }
+        if (outputPadding.size() != rank - 2) {
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "output_padding list size does not match the number of axes");
+        }
+
+        SmallVector<Value> cstPadding, cstStrides, cstDilations,
+            cstOutputPadding;
+        if (padding.size() != 2 * (rank - 2)) {
+          for (int64_t i : padding) {
+            cstPadding.push_back(rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+          }
+        } else {
+          for (unsigned i = 0; i < padding.size() / 2; i++) {
+            if (padding[i] != padding[i + (padding.size() / 2)]) {
+              // TODO: Add support for different padding values for the
+              // beginning and ending along each spatial axis
+              return rewriter.notifyMatchFailure(
+                  binder.op,
+                  "unsupported conversion: padding values for the beginning "
+                  "and ending along each spatial axis must be equal");
+            }
+            cstPadding.push_back(rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getI64IntegerAttr(padding[i])));
+          }
+        }
+        for (int64_t i : dilations) {
+          cstDilations.push_back(rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+        }
+        for (int64_t i : strides) {
+          cstStrides.push_back(rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+        }
+        for (int64_t i : outputPadding) {
+          cstOutputPadding.push_back(rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+        }
+
+        Value paddingList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstPadding);
+        Value dilationsList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstDilations);
+        Value stridesList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstStrides);
+        Value outputPaddingList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            cstOutputPadding);
+        Value transposed =
+            rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
+        Value bias;
+        if (binder.op->getNumOperands() == 3) {
+          if (binder.tensorOperandAtIndex(bias, 2)) {
+            return failure();
+          }
+        } else {
+          bias = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+        }
+        Value cstGroup = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(group));
+
+        rewriter.replaceOpWithNewOp<Torch::AtenConvolutionOp>(
+            binder.op, resultType, input, weight, bias, stridesList,
+            paddingList, dilationsList, transposed, outputPaddingList,
+            cstGroup);
+        return success();
+      });
   patterns.onOp("Cos", 7,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
index 5f36d1bbf..438d6c7ad 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
@@ -462,3 +462,133 @@ func.func @test_averagepool_3d_default(%arg0: !torch.vtensor<[1,3,32,32,32],f32>
   %0 = torch.operator "onnx.AveragePool"(%arg0) {torch.onnx.kernel_shape = [2 : si64, 2 : si64, 2 : si64]} : (!torch.vtensor<[1,3,32,32,32],f32>) -> !torch.vtensor<[1,3,31,31,31],f32>
   return %0 : !torch.vtensor<[1,3,31,31,31],f32>
 }
+
+// CHECK-LABEL: @test_conv_with_strides_no_padding
+func.func @test_conv_with_strides_no_padding(%arg0: !torch.vtensor<[1,1,7,5],f32>, %arg1: !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,3,2],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[C0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[C1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[C2:.*]] = torch.constant.int 2
+  // CHECK: %[[C2_0:.*]] = torch.constant.int 2
+  // CHECK: %[[C0_1:.*]] = torch.constant.int 0
+  // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0_1]], %[[C0_1]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,7,5],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,3,2],f32>
+  %0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,7,5],f32>, !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,3,2],f32>
+  return %0 : !torch.vtensor<[1,1,3,2],f32>
+}
+
+// CHECK-LABEL: @test_conv_with_strides_padding
+func.func @test_conv_with_strides_padding(%arg0: !torch.vtensor<[1,1,7,5],f32>, %arg1: !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,4,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[C1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+  // CHECK: %[[C2:.*]] = torch.constant.int 2
+  // CHECK: %[[C2_0:.*]] = torch.constant.int 2
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,7,5],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,3],f32>
+  %0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,7,5],f32>, !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,4,3],f32>
+  return %0 : !torch.vtensor<[1,1,4,3],f32>
+}
+
+// CHECK-LABEL: @test_convtranspose_dilations
+func.func @test_convtranspose_dilations(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,1,2,2],f32>) -> !torch.vtensor<[1,1,5,5],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[C0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[C2:.*]] = torch.constant.int 2
+  // CHECK: %[[C2_0:.*]] = torch.constant.int 2
+  // CHECK: %[[C1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[C0_1:.*]] = torch.constant.int 0
+  // CHECK: %[[C0_2:.*]] = torch.constant.int 0
+  // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0_1]], %[[C0_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,1,2,2],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,5,5],f32>
+  %0 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.dilations = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,1,2,2],f32>) -> !torch.vtensor<[1,1,5,5],f32>
+  return %0 : !torch.vtensor<[1,1,5,5],f32>
+}
+
+// CHECK-LABEL: @test_convtranspose
+func.func @test_convtranspose(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,5,5],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[C0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[C1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+  // CHECK: %[[C0_1:.*]] = torch.constant.int 0
+  // CHECK: %[[C0_2:.*]] = torch.constant.int 0
+  // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0_1]], %[[C0_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,5,5],f32>
+  %0 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,5,5],f32>
+  return %0 : !torch.vtensor<[1,2,5,5],f32>
+}
+
+// CHECK-LABEL: @test_convtranspose_pad
+  func.func @test_convtranspose_pad(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,10,8],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+    // CHECK: %[[C0:.*]] = torch.constant.int 0
+    // CHECK: %[[C0_0:.*]] = torch.constant.int 0
+    // CHECK: %[[C1:.*]] = torch.constant.int 1
+    // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+    // CHECK: %[[C3:.*]] = torch.constant.int 3
+    // CHECK: %[[C2:.*]] = torch.constant.int 2
+    // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+    // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+    // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C3]], %[[C2]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
+    // CHECK: %[[BIAS:.*]] = torch.constant.none
+    // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+    // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,10,8],f32>
+    %0 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.output_padding = [1 : si64, 1 : si64], torch.onnx.strides = [3 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,10,8],f32>
+    return %0 : !torch.vtensor<[1,2,10,8],f32>
+  }
+
+// CHECK-LABEL: @test_convtranspose_pads
+  func.func @test_convtranspose_pads(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,7,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+    // CHECK: %[[C1:.*]] = torch.constant.int 1
+    // CHECK: %[[C2:.*]] = torch.constant.int 2
+    // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+    // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+    // CHECK: %[[C3:.*]] = torch.constant.int 3
+    // CHECK: %[[C2_0:.*]] = torch.constant.int 2
+    // CHECK: %[[C0:.*]] = torch.constant.int 0
+    // CHECK: %[[C0_1:.*]] = torch.constant.int 0
+    // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C1]], %[[C2]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_0]], %[[C1_1]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C3]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_1]] : (!torch.int, !torch.int) -> !torch.list<int>
+    // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
+    // CHECK: %[[BIAS:.*]] = torch.constant.none
+    // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+    // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,7,3],f32>
+    %0 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.pads = [1 : si64, 2 : si64, 1 : si64, 2 : si64], torch.onnx.strides = [3 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,3,3],f32>) -> !torch.vtensor<[1,2,7,3],f32>
+    return %0 : !torch.vtensor<[1,2,7,3],f32>
+  }