fix: call weight_loader in Step35ShardingStrategy for distributed transfer

Step35ShardingStrategy.shard_model() accepted the weight_loader parameter
but never called it, unlike all other sharding strategies. This meant
receiver nodes using distributed weight broadcast with tensor parallelism
on Step35 models would get zero/garbage weights after sharding.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

src/exo/worker/engines/mlx/auto_parallel.py

@@ -1041,7 +1041,9 @@ class Step35ShardingStrategy(TensorParallelShardingStrategy):
     ) -> nn.Module:
         model = cast(Step35Model, model)
 
-        for layer in model.layers:
+        for i, layer in enumerate(model.layers):
+            if weight_loader is not None:
+                weight_loader(model, i)
             eval_with_timeout(
                 layer.parameters(), timeout_seconds / len(model.layers), on_timeout
             )