[BugFix][Relax][Torch] Honor correction in std/var converter

[swjng]

Motivation

The PyTorch frontend's _var ignored the correction kwarg of
aten.var.correction. torch.export.run_decompositions() rewrites
both aten.std.correction and aten.std.dim into
aten.var.correction(..., correction=<value>) → sqrt, so every
torch.std/torch.var call lands in _var — but the correction
value was dropped on the floor. The variance was therefore always
divided by n regardless of what the user requested.

Minimal repro (vs PyTorch eager):

x = [[1, 2, 3, 4, 5], [2, 2, 2, 2, 2]]
torch.std(x, dim=1, unbiased=True)
  ref: [1.5811, 0.0]   # sqrt(2.5)
  tvm: [1.4142, 0.0]   # sqrt(2.0) -- correction silently set to 0

The same omission shows up for explicit torch.var(x, correction=k)
and any model that relies on the documented Bessel default.

Fix

Route aten.var.correction (identified by OpOverload._overloadname,
not a substring match) to a new _var_correction helper. It reads
correction from node.kwargs, treats None as 1 to match the
overload's Scalar? correction = None schema, and scales the
existing relax.op.variance output by n / (n - correction) when
correction != 0.

When n - correction <= 0, the multiplier is set to NaN rather than
raising — this mirrors PyTorch's documented
max(0, N - correction) semantics (eager produces NaN with a warning,
not an error).

Reduction-axis sizes are read from x.struct_info.shape. Dynamic
sizes raise NotImplementedError; static-shape models cover the
real-world torch.export flow.

The legacy fx path through _var is intentionally left alone — it has
a separate preexisting bug (it reads args[2] as keepdim even when
that slot is unbiased), but fixing that here would expand the scope
of this PR beyond the correction semantics.

Notes

• _std is also registered for "std.correction" but is unreachable on
  the default exported-program path because aten.std.* always
  decomposes to var.correction + sqrt before dispatch. Sparse-tensor
  exports that skip run_decompositions still hit the old _std; that
  path is out of scope for this fix.
• Existing test_std/test_var encoded the buggy correction=0 IR
  for torch.var(x) (which defaults to Bessel) and have been updated
  to expect the correct R.multiply(var, R.const(15/14)). New
  test_var_correction covers explicit correction=2 and correction=0.

[gemini-code-assist]

Code Review

This pull request implements support for the correction argument in torch.var and torch.std operations by calculating a scaling factor based on the reduction size. The feedback identifies the need to handle non-integer dim types in _reduction_size to avoid type errors, resolve the correction argument from the FX graph to handle symbolic values, and maintain naming consistency for tirx.IntImm.

[tlopex]

LGTM