[Relax][Onnx] Pass output_padding param in ConvTranspose (#18635)

### Summary
Inconsistent shapes of results produced by TVM and ONNX View due to the
ConvTranspose operator

### Steps to Reproduce
- ONNX View: Output Shape = (1, 6, 56, 56)
<img width="500" height="350" alt="Screenshots"
src="https://github.com/user-attachments/assets/8a7129b4-9ebc-47a0-aa47-6060e8df2827"
/>

- TVM:  Output Shape = (1, 6, 55, 55) (due to output_padding=[0, 0])
```
class Module:
    def main(input: R.Tensor((1, 3, 28, 28), dtype="float32"), weight: R.Tensor((3, 6, 3, 3), dtype="float32"), bias: R.Tensor((6,), dtype="float32")) -> R.Tensor((1, 6, 55, 55), dtype="float32"):
        R.func_attr({"num_input": 1, "params": [metadata["ffi.Tensor"][0], metadata["ffi.Tensor"][1]]})
        with R.dataflow():
            lv: R.Tensor((1, 6, 55, 55), dtype="float32") = R.nn.conv2d_transpose(input, weight, strides=[2, 2], padding=[1, 1, 1, 1], output_padding=[0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="IOHW", out_layout="NCHW", out_dtype="void")
            lv1: R.Tensor((1, 6, 1, 1), dtype="float32") = R.reshape(bias, R.shape([1, 6, 1, 1]))
            gv: R.Tensor((1, 6, 55, 55), dtype="float32") = R.add(lv, lv1)
            R.output(gv)
        return gv
```

### Expected
- output_padding = [1, 1]
```
class Module:
    def main(input: R.Tensor((1, 3, 28, 28), dtype="float32"), weight: R.Tensor((3, 6, 3, 3), dtype="float32"), bias: R.Tensor((6,), dtype="float32")) -> R.Tensor((1, 6, 56, 56), dtype="float32"):
        R.func_attr({"num_input": 1, "params": [metadata["ffi.Tensor"][0], metadata["ffi.Tensor"][1]]})
        with R.dataflow():
            lv: R.Tensor((1, 6, 56, 56), dtype="float32") = R.nn.conv2d_transpose(input, weight, strides=[2, 2], padding=[1, 1, 1, 1], output_padding=[1, 1], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="IOHW", out_layout="NCHW", out_dtype="void")
            lv1: R.Tensor((1, 6, 1, 1), dtype="float32") = R.reshape(bias, R.shape([1, 6, 1, 1]))
            gv: R.Tensor((1, 6, 56, 56), dtype="float32") = R.add(lv, lv1)
            R.output(gv)
        return gv
```

### Resolve
- When implement converts an onnx ConvTranspose node into an equivalent
Relax expression, get and pass output_padding param into op.
- Fixed: #18601

Author: locnd182644

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -1334,6 +1334,7 @@ def _impl_v1(cls, bb, inputs, attr, params):
             weight=inputs[1],
             strides=attr.get("strides", 1),
             padding=attr.get("pads", 0),
+            output_padding=attr.get("output_padding", 0),
             dilation=attr.get("dilations", 1),
             groups=attr.get("group", 1),
             data_layout=data_layout,

src/relax/op/nn/convolution.cc

@@ -786,7 +786,7 @@ Expr conv2d_transpose(Expr data, Expr weight, ffi::Array<IntImm> strides,
   CHECK_GT(groups, 0) << "The number of groups in convolution is expected to be positive. However, "
                          "the given number of groups is "
                       << groups;
-  CHECK_EQ(output_padding.size(), 2) << "The input output_padding length is expected to be 4. "
+  CHECK_EQ(output_padding.size(), 2) << "The input output_padding length is expected to be 2. "
                                         "However, the given output_padding is "
                                      << output_padding;
   CHECK_EQ(strides.size(), 2)

tests/python/relax/test_frontend_onnx.py

@@ -1171,11 +1171,12 @@ def _verify_conv(input_shape, weight_shape):
     _verify_conv([3, 4, 32, 32, 32], [2, 4, 3, 3, 3])  # group=2
 
 
-@pytest.mark.parametrize("stride", [1, 2])
+@pytest.mark.parametrize("stride", [2])
 @pytest.mark.parametrize("dilation", [1])
 @pytest.mark.parametrize("bias", [True, False])
 @pytest.mark.parametrize("pad", [0, 2])
-def test_conv_transpose(stride: int, dilation: int, pad: int, bias: bool):
+@pytest.mark.parametrize("output_pad", [0, 1])
+def test_conv_transpose(stride: int, dilation: int, pad: int, bias: bool, output_pad: int):
     def _verify_conv_transpose(input_shape, weight_shape):
         nd = len(weight_shape) - 2
         output_shape = [input_shape[0], weight_shape[0]] + [
@@ -1190,6 +1191,7 @@ def _verify_conv_transpose(input_shape, weight_shape):
             strides=[stride] * nd,
             dilations=[dilation] * nd,
             pads=[pad] * nd * 2,
+            output_padding=[output_pad] * nd,
             group=input_shape[1] // weight_shape[1],
         )
         graph = helper.make_graph(