## Description
When `from_onnx(model, keep_params_in_input=True)` is used, every ONNX
initializer becomes a `relax.Var` instead of a `relax.Constant`. The
`Concat` handler's `is_shape_like()` check only recognizes
`relax.ShapeExpr` and 1D-int64 `relax.Constant`, so a 1D-int64 shape
value loaded as a Var is no longer recognized.
When such a Var is concatenated with a `ShapeExpr` — the standard
pattern for dynamic-batch `Reshape` in PyTorch-exported ONNX models —
the heterogeneous `Tuple(ShapeExpr, Tensor)` is rejected by
`relax.op.concat` with:
```
InternalError: Op(relax.concat) expects the input to be a Tuple of Tensors.
However, the given input is R.Tuple(R.Shape([N]), R.Tensor((1,), dtype="int64"))
```
This effectively breaks `keep_params_in_input=True` for any model with
dynamic-batch `Reshape` (extremely common in PyTorch ONNX exports).
## Fix
Run each `Concat` input through the existing `get_constant` helper
before the `is_shape_like` check. This resolves any `Var` that maps to a
known param back to its baked `Constant`, restoring the all-shape-like
fast path.
## Minimal repro
An 8-node ONNX graph (`Shape` → `Slice` → `Concat([dyn_n, [12]])` →
`Reshape`) fails with `keep_params_in_input=True` before this PR and
passes after. A regression test (`test_concat_with_param_shape_value`)
covers this pattern.
## Testing
```
pytest tests/python/relax/test_frontend_onnx.py -k concat
```
9 passed (1 new + 8 existing).
Soowon Jeong
82a37dac1d