[BugFix][Relax][Torch] Honor multi-axis dims in torch.flip converter

[swjng]

Motivation

PyTorch's torch.flip(x, dims=[...]) reverses every listed axis. The
Relax converter _flip (base_fx_graph_translator.py) instead coerces
the list to a single integer:

if isinstance(dims, list | tuple) and len(dims) > 0:
    dims = dims[0]

Only the first axis is forwarded to relax.op.flip, which is itself
single-axis. The remaining axes are silently dropped.

Minimal repro (vs PyTorch eager) on a (3, 4) input with
dims=[-1, -2]:

ref: [11, 10,  9,  8,  7,  6,  5,  4, ...]   # both axes flipped
tvm: [ 3,  2,  1,  0,  7,  6,  5,  4, ...]   # only last axis flipped

max_abs_diff = 8.0. Both the torch.export and legacy fx paths share
this converter, so both are affected.

Fix

Iterate over dims in the converter and emit one relax.op.flip per
axis (flips along distinct axes commute, so the order is irrelevant).
A scalar dims is wrapped to a single-element list; non-int /
non-sequence arguments still raise TypeError.

relax.op.flip itself is unchanged: it is used elsewhere as a
single-axis op, and widening its signature would expand the scope of
this fix beyond the PyTorch frontend.

[gemini-code-assist]

Code Review

This pull request updates the _flip translator in the Relax Torch frontend to support multi-axis flipping, aligning with torch.flip semantics. The implementation now iterates through the provided dimensions and emits a relax.op.flip operation for each axis, whereas it previously only supported a single axis. Additionally, new test cases have been added to the FX and exported program frontend test suites to verify multi-axis and negative axis support. I have no feedback to provide as there are no review comments.

[tlopex]

LGTM