fix(te-plugin): handle TE 2.15+ tuple return from _Linear / _GroupedLinear (#1481)

### What does this PR do?

Type of change: Bug fix

TE 2.15+ changed `_Linear.forward` and `_GroupedLinear.forward` to
return `(out, new_workspace)` tuples instead of a single tensor.
ModelOpt's patched `te_quantized_linear_fn` /
`te_grouped_quantized_linear_fn` still piped the whole tuple into
`self.output_quantizer`, crashing inside `TensorQuantizer.forward` on
`tuple.numel()`:

```
File ".../modelopt/torch/quantization/plugins/transformer_engine.py", line 184, in te_grouped_quantized_linear_fn
    return self.output_quantizer(output)
File ".../tensor_quantizer.py", line 1037, in forward
    if inputs.numel() == 0:
AttributeError: 'tuple' object has no attribute 'numel'
```

Mirror the existing pattern from `_QuantTELayerNormLinear.forward`: when
the underlying TE call returns a tuple, quantize only `output[0]` (the
activation tensor) and pass auxiliary workspace metadata through
unchanged. TE <= 2.14 returns a single tensor and falls through the
`isinstance` branch identically to before this change.

Already landed on `release/0.44.0` as commit `c897fbeaaf`; this brings
`main` in sync. Follow-up to
[#1473](#1473) (signature
introspection + `_forward` cache lookup), which fixed an earlier symptom
of the same TE 2.15 signature change but not this tuple-return path.

### Usage

No public API change. PTQ continues to work transparently across TE 2.x:

```python
import modelopt.torch.quantization as mtq
mtq.quantize(model, mtq.NVFP4_DEFAULT_CFG, forward_loop)  # now works on TE 2.15.x
```

### Testing
<!-- Mention how have you tested your change if applicable. -->

Verified locally against **both TE 2.12** and **TE 2.15.0** using:

```bash
pytest tests/gpu_megatron/torch/quantization/plugins/test_transformer_engine.py
```

Without this fix on TE 2.15, the same test fails immediately with
`AttributeError: 'tuple' object has no attribute 'numel'`. With this
fix, both versions exercise the same code paths and pass — TE <= 2.14
skips the `isinstance(output, tuple)` branch and behaves identically to
before.

### Before your PR is "*Ready for review*"

Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).

Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ <!--- Public API unchanged; TE
<= 2.14 path is identical (isinstance branch is false). -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: N/A <!--- Existing
`test_transformer_engine.py` already exercises both paths; it would have
caught this on TE 2.15 had CI been running against that version. A
TE-version matrix is the right follow-up but is out of scope here. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A

### Additional Information
<!-- E.g. related issue. -->
Triggered by Megatron-Bridge failing tests after their TE 2.15 bump. The
`release/0.44.0` cherry-pick was pushed directly (commit `c897fbeaaf`)
so Bridge could unblock; this PR carries the same fix forward to main.

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>

Author: kevalmorabia97

modelopt/torch/quantization/plugins/transformer_engine.py

@@ -100,6 +100,10 @@ def te_quantized_linear_fn(package, func_name, self, *args, **kwargs):
         new_args[weight_pos] = self.weight_quantizer(args[weight_pos])
         new_args[inp_pos] = self.input_quantizer(args[inp_pos])
         output = getattr(package, func_name)(*new_args, **kwargs)
+        # TE 2.15+ returns `(out, new_weight_workspace)`; TE <= 2.14 returns just `out`.
+        # Only the activation tensor participates in output quantization.
+        if isinstance(output, tuple):
+            return (self.output_quantizer(output[0]), *output[1:])
         return self.output_quantizer(output)
 
     # Override the quantized linear function
@@ -195,6 +199,10 @@ def te_grouped_quantized_linear_fn(package, func_name, self, *args):
         for i in range(weights_start, weights_start + num_gemms):
             new_args[i] = self.weight_quantizer(args[i])
         output = getattr(package, func_name)(*new_args)
+        # TE 2.15+ returns `(out, new_workspaces)`; TE <= 2.14 returns just `out`.
+        # Only the activation tensor participates in output quantization.
+        if isinstance(output, tuple):
+            return (self.output_quantizer(output[0]), *output[1:])
         return self.output_quantizer(output)
 
     # Override the quantized linear function