tests/studio: accept new grad_norm arg in MLX smoke _on_step callback

The MLX trainer's step callback now passes a ninth positional argument
(grad_norm) per unsloth_zoo/mlx/trainer.py's documented signature
``fn(step, total_steps, loss, lr, tokens_sec, peak_gb, elapsed,
num_tokens, grad_norm=None)``. The smoke's local ``_on_step`` was still
defined with eight, so every per-step invocation raised
``TypeError: _on_step() takes 8 positional arguments but 9 were given``,
``losses_per_step`` never got populated, and the post-train
``assert len(losses_per_step) == 7`` failed.

Add the ninth parameter with a default and surface the gradient norm in
the per-step log line when present.

tests/studio/run_real_mlx_smoke.py

@@ -296,11 +296,16 @@ def cmd_train(args) -> int:
             args = config,
         )
 
-        def _on_step(step, total, loss, lr, tok_s, peak_gb, elapsed, num_tokens):
+        def _on_step(step, total, loss, lr, tok_s, peak_gb, elapsed,
+                     num_tokens, grad_norm = None):
             losses_per_step.append(round(float(loss), 4))
+            grad_text = (
+                f"  grad={grad_norm:.4f}"
+                if grad_norm is not None else ""
+            )
             print(
                 f"  step {step}/{total}  loss={loss:.4f}  lr={lr:.2e}  "
-                f"tok/s={tok_s:.0f}  peak={peak_gb:.2f}GB",
+                f"tok/s={tok_s:.0f}  peak={peak_gb:.2f}GB{grad_text}",
                 flush = True,
             )