[Bug] Output mismatch with ONNXRuntime for valid model using GELU + Interpolate + InstanceNorm2d

### Expected behavior

Consistent Result between TVM and ONNXRuntime

### Actual behavior

`––––– MISMATCH DETECTED –––––

Not equal to tolerance rtol=0.01, atol=0.001

Mismatched elements: 65536 / 65536 (100%)
Max absolute difference: 0.00585903
Max relative difference: 0.8311606
x: array([[[[0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119],
[0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119],
[0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119],...
y: array([[[[0.007049, 0.007049, 0.007049, ..., 0.007049, 0.007049,
0.007049],
[0.007049, 0.007049, 0.007049, ..., 0.007049, 0.007049,...`

### Environment

TVM: 0.17.0
ONNXRuntime: 1.16.3

### Steps to reproduce

```python
import torch
import torch.nn as nn
import onnx
import onnxruntime as ort
import numpy as np
import tempfile
import torch.nn.functional as F
import tvm
from tvm import relay
from tvm.contrib import graph_executor
from numpy.testing import assert_allclose

class SimpleBugModel(nn.Module):
def __init__(self):
super().__init__()

self.input_conv = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=1)
self.block5 = nn.InstanceNorm2d(num_features=16)

def forward(self, x):
x = self.input_conv(x)
x=F.softplus(x)
x = torch.tanh(x)
x = torch.ceil(x)
x = F.gelu(x)
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.block5(x)
return x

def main():
model = SimpleBugModel()
model.eval()
dummy = torch.randn(1, 3, 32, 32, dtype=torch.float32)
with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmp:
onnx_path = tmp.name
torch.onnx.export(
model, dummy, onnx_path,
opset_version=19,
input_names=["input"],
output_names=["output"],
)

## run ONNX-Runtime
ort_sess = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
ort_out = ort_sess.run(None, {"input": dummy.numpy()})[0]

### compile & run TVM
onnx_model = onnx.load(onnx_path)
shape_dict = {"input": dummy.numpy().shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, freeze_params=True)
with tvm.transform.PassContext(opt_level=4):
lib = relay.build(mod, target="llvm", params=params)
m = graph_executor.GraphModule(lib["default"](tvm.cpu()))
m.set_input("input", tvm.nd.array(dummy.numpy()))
m.run()
tvm_out = m.get_output(0)
tvm_out = tvm_out.numpy()

try:
assert_allclose(ort_out, tvm_out, rtol=1e-2, atol=1e-3, equal_nan=True)
except AssertionError as e:
print("––––– MISMATCH DETECTED –––––")
print(e) # just the assertion message
except Exception as e:
print("––––– UNEXPECTED ERROR DURING COMPARISON –––––")
print(f"{type(e).__name__}: {e}")

if __name__ == "__main__":
main()

```

Start by running the provided Python reproducer with TVM 0.17.0 and ONNXRuntime 1.16.3, then inspect the relay.frontend.from_onnx import path and relay.build handling of the GELU, Interpolate, and InstanceNorm2d sequence. Done means the TVM and ONNXRuntime outputs agree within the reported rtol=0.01 and atol=0.001 tolerance.

Written by the indexing model from the issue text.