[Bug] [FRONTEND][ONNX] Error converting operator ConvTranspose: InternalError: In Op(relax.add), the first input shape at dim 1 is T.int64(16) and the second input shape at dim 1 is T.int64(32), which are not broadcastable.

### Expected behavior

TVM should run the model correctly.

### Actual behavior

For the following simple model,

it can be executed by onnxruntime and onnx's ReferenceEvaluator, the results are as follows:
```
[[[[-14.942039 25.242477 12.100965 ... 57.370975
43.9531 2.7949858 ]
[ 10.37292 -10.740159 -38.21392 ... -25.30576
20.154476 -19.093735 ]
[ 23.041002 41.820953 -34.108677 ... 13.982165
7.2081375 11.132856 ]
...
[ -5.590187 -30.208424 9.572759 ... 32.32894
10.533618 -10.12186 ]
[-11.91229 29.118868 -20.662777 ... -16.910463
-3.0499296 -8.469831 ]
[ -1.340055 -17.950693 1.7485574 ... 44.479458
11.766148 -29.151772 ]]

[[ 3.5306716 -17.110134 -12.246135 ... 3.6663613
10.713657 18.570526 ]
[ 14.867607 -7.739101 -32.685593 ... -8.97093
-14.347578 32.1913 ]
[ 7.462762 -14.961873 -18.268976 ... -16.221258
9.145517 22.871971 ]
...
[ 58.016407 -28.310326 27.333632 ... 33.982685
-0.22384354 44.509457 ]
[-27.212149 -17.831676 7.140829 ... 10.431597
33.523167 -36.84701 ]
[ 4.2589498 -18.459444 9.673733 ... -21.87571
11.560403 -11.319146 ]]

[[ 1.6221294 -24.357258 37.832687 ... -15.675017
17.112104 -19.541176 ]
[ -1.9535408 -43.395298 -7.589774 ... 32.80785
39.018574 -9.826303 ]
[ 23.802937 -22.675396 2.9990005 ... -29.012583
55.184933 16.124914 ]
...
[ 13.447808 1.2474155 68.31904 ... -33.191654
7.361887 -14.480252 ]
[ 8.724002 -44.378754 -3.259966 ... -43.776226
47.07514 4.846405 ]
[-28.980995 0.75377727 -20.248524 ... -13.6773405
34.299976 -5.997253 ]]

...

[[-33.108776 -16.460379 7.451367 ... -3.0716655
8.792357 9.199628 ]
[-10.372536 -22.135944 -21.878239 ... 55.7764
-7.191108 -14.206541 ]
[-53.47607 10.408365 -20.441448 ... 23.109488
1.2017591 6.3908386 ]
...
[ -0.9444683 -29.720179 -36.938797 ... -32.23233
-35.5812 42.359478 ]
[-28.41974 -0.1681884 -38.875988 ... 14.968706
-23.108547 -32.480797 ]
[ 2.9518757 1.1772063 -21.35058 ... -24.29589
1.791322 -2.575415 ]]

[[ -1.1625404 -6.98105 -10.189097 ... -26.975689
-14.790653 -8.432305 ]
[ 17.315321 34.95646 -33.681236 ... -8.27439
9.273292 -20.079172 ]
[ -9.413344 25.616909 -8.439555 ... -17.31526
18.547462 -18.068047 ]
...
[ 0.11532002 -46.406166 -29.583313 ... -17.339268
17.583694 38.199493 ]
[-49.912685 10.065314 -67.41777 ... -17.945366
-17.426779 24.320415 ]
[ -9.248135 4.745124 -5.5740147 ... 33.417686
-3.3946886 4.773201 ]]

[[-11.940619 1.2533135 -15.480752 ... 17.695894
23.379114 -6.3436594 ]
[ -2.8251894 -1.4625757 12.186805 ... -25.161877
6.0388117 -8.681617 ]
[ 37.58026 7.1358566 -53.616978 ... 49.06318
-9.981028 15.518544 ]
...
[-19.931892 23.587257 -68.27191 ... -25.605322
-31.568523 0.6990657 ]
[ 56.762306 -4.785701 3.260337 ... 2.4171884
-50.66727 -23.977222 ]
[ 13.220358 -13.136884 18.563856 ... -9.229172
-32.588108 27.032162 ]]]]
```

However, the onnx frontend of TVM cannot import it:
```
File "/home/ubuntu/Documents/test1.py", line 49, in test
tvm_model = from_onnx(onnx_model, opset=14, keep_params_in_input=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/Documents/DLCompilers/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 4260, in from_onnx
return g.from_onnx(graph, opset)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/Documents/DLCompilers/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 3890, in from_onnx
self._construct_nodes(graph)
File "/home/ubuntu/Documents/DLCompilers/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 4071, in _construct_nodes
raise err
File "/home/ubuntu/Documents/DLCompilers/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 4068, in _construct_nodes
op = self.bb.normalize(op)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/Documents/DLCompilers/tvm/python/tvm/relax/block_builder.py", line 672, in normalize
return _ffi_api.BlockBuilderNormalize(self, expr) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "python/tvm_ffi/cython/function.pxi", line 904, in tvm_ffi.core.Function.__call__
File "", line 0, in tvm::relax::Normalizer::Normalize(tvm::RelaxExpr const&)
File "", line 0, in tvm::relax::ExprFunctor::VisitExpr(tvm::RelaxExpr const&)
File "", line 0, in tvm::relax::ExprFunctor::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor*)#9}::_FUN(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor*)
File "", line 0, in tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)
File "", line 0, in tvm::relax::Normalizer::InferStructInfo(tvm::relax::Call const&)
File "", line 0, in tvm::relax::InferStructInfoBroadcastArith(tvm::relax::Call const&, tvm::relax::BlockBuilder const&)
File "", line 0, in tvm::relax::StructInfo tvm::relax::InferStructInfoBroadcast(tvm::relax::Call const&, tvm::relax::BlockBuilder const&, tvm::runtime::DataType (*)(tvm::relax::Call const&, tvm::relax::BlockBuilder const&, tvm::relax::StructInfo const&, tvm::relax::StructInfo const&))
File "", line 0, in tvm::relax::InferBinaryBroadcastShape(tvm::relax::Call const&, tvm::relax::BlockBuilder const&, tvm::ffi::Array const&, tvm::ffi::Array const&)
File "", line 0, in tvm::relax::BlockBuilderImpl::ReportFatal(tvm::Diagnostic const&)
File "", line 0, in tvm::runtime::detail::LogFatal::Entry::Finalize()
tvm.error.InternalError: In Op(relax.add), the first input shape at dim 1 is T.int64(16) and the second input shape at dim 1 is T.int64(32), which are not broadcastable.
[11:03:35] /home/ubuntu/Documents/DLCompilers/tvm/src/relax/ir/block_builder.cc:64: Warning: BlockBuilder destroyed with remaining blocks!
```

### Environment

OS: Ubuntu 20.04
TVM: 0.23.dev0 (https://github.com/apache/tvm/commit/f4e28d3153323ad97a7e74740c9fb22300fd6cd0)

onnxruntime: 1.23.2

### Steps to reproduce

This bug can be reproduced by the following code with the model in the attachment.
```python
from typing import Dict, List, Literal, Optional
import sys
import os

import numpy as np
import onnx
from onnx.reference import ReferenceEvaluator

import onnxruntime
from onnx import ModelProto, TensorProto, helper

import tvm
import tvm.testing
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx

import argparse
import pickle

def test() -> None:
onnx_model = onnx.load("11.onnx")
# Configure model format.
onnx_model.ir_version = 8
onnx_model.opset_import[0].version = 14

with open("inputs.pkl", 'rb') as fp:
inputs = pickle.load(fp)
# Run the model through onnx to get the expected result.
try:
ort_session = onnxruntime.InferenceSession(
onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]
)
ort_output = ort_session.run([], inputs)
except Exception as e:
print(e)
print("This model cannot be executed by onnxruntime!")
sys.exit(1)

print("onnxruntime:", ort_output[0])

# ReferenceEvaluator
sess = ReferenceEvaluator("11.onnx")
re_output = sess.run(None, inputs)
print("ReferenceEvaluator:", re_output[0])

tvm.testing.assert_allclose(re_output[0], ort_output[0], rtol=0.1, atol=0.1)

# TVM
tvm_model = from_onnx(onnx_model, opset=14, keep_params_in_input=True)

if __name__ == "__main__":
test()

```

[testcase.zip](https://github.com/user-attachments/files/24303049/testcase.zip)

### Triage

Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below).

* needs-triage

cc @KJlaccHoeUM9l

Start by running the supplied reproduction with testcase.zip and inspect python/tvm/relax/frontend/onnx/onnx_frontend.py, especially from_onnx and the node construction path. Trace the ConvTranspose conversion through BlockBuilder normalization and relax.add; done means the model imports without the broadcast-shape error and agrees with the onnxruntime and ReferenceEvaluator results.

Written by the indexing model from the issue text.