[Bug] [Relax ONNX front]ONNX frontend import failed with shape graph Concat error

### Expected behavior

The ONNX model should be successfully imported by the TVM Relax ONNX frontend.

The model contains dynamic shape calculation subgraphs before `Reshape`.

The shape graph operators, such as:

- `Shape`
- `Gather`
- `Slice`
- `Unsqueeze`
- `Concat`

should be converted into Relax `ShapeExpr` / `PrimValue` instead of normal tensor operations.

---

### Actual behavior

TVM Relax ONNX frontend fails during import.

Error:

```
InternalError:
Concat expects all input tensors to have same ndim.
However, the input contains tensors with ndim 3 and 2
```

The failure happens during `Concat` conversion.

The problematic `Concat` belongs to a dynamic shape construction subgraph, but it is handled as a normal tensor concatenation operation.

---

### Environment

- TVM: `0.25.dev0` (local source tree)
- PyTorch: `2.12.0+cu130`
- Python: `3.10.20`
- OS: `Ubuntu 22.04 64-bit`
- Target: `llvm` (CPU)
- Frontend: from tvm.relax.frontend.onnx import from_onnx

---

### Steps to reproduce

Load an ONNX model containing a dynamic reshape pattern:

```python
import onnx

from tvm.relax.frontend.onnx import from_onnx

model = onnx.load("model.onnx")

mod = from_onnx(model)
```

The import fails during `Concat` conversion:

```python
InternalError(
'Concat expects all input tensors to have same ndim.
However, the input contains tensors with ndim 3 and 2'
)
```

---

### Analysis

The failure occurs for ONNX graphs containing dynamic reshape patterns.

Example pattern:

```
Input tensor
|
Shape
|
Gather / Slice
|
Unsqueeze
|
Concat
|
Reshape
```

The ONNX graph generates the target shape of `Reshape` dynamically.

These intermediate operators do not perform tensor computation. They only construct the shape argument required by `Reshape`.

The expected Relax representation is:

```
ShapeExpr
|
PrimValue
|
ShapeExpr
|
Concat
|
ShapeExpr
```

The current issue is that the shape graph `Concat` is incorrectly handled as tensor `Concat`, causing the dimension mismatch error.

---

### Root cause

The same ONNX operators can appear in both tensor computation graphs and shape computation graphs.

Tensor graph example:

```
Conv
|
Concat
|
Relu
```

Shape graph example:

```
Shape
|
Gather
|
Unsqueeze
|
Concat
|
Reshape
```

The frontend needs data-flow based detection to distinguish these two cases.

Start by running the supplied from_onnx reproduction with a model containing the Shape/Gather/Slice/Unsqueeze/Concat/Reshape pattern, then trace the Relax ONNX frontend's Concat conversion. Compare its handling of tensor and shape-graph inputs. Done means dynamic shape operators produce ShapeExpr/PrimValue values and the model imports without the ndim mismatch, while ordinary tensor Concat remains unchanged.

Written by the indexing model from the issue text.