Make the example's __main__ actually run

It claimed to run the pipeline with no engine involved, and could not: the
node bodies it called save and load artifacts, which raise outside a node by
design. Each node is now a thin wrapper over a plain function — make_rows,
train_curve, score — and __main__ calls those, which is the split the sandbox
already demonstrates and the one worth copying.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

examples/myresearch/pipeline.py

@@ -12,9 +12,9 @@ provides a message another requires, never because one imported the other.
 from __future__ import annotations
 
 from fluksio import Flow, Port, use
-from myresearch.data import augment, prepare
-from myresearch.evaluate import evaluate
-from myresearch.train import fit
+from myresearch.data import augment, make_rows, prepare
+from myresearch.evaluate import evaluate, score
+from myresearch.train import fit, train_curve
 
 train = Flow(
     "train",
@@ -41,8 +41,11 @@ finetune = Flow(
 
 
 if __name__ == "__main__":
-    # Run it here, with no engine involved: the decorators changed nothing
-    # about calling these functions.
-    dataset = prepare(limit=64)
-    losses = list(fit(dataset["dataset"], lr=0.05, epochs=5))
-    print("losses:", [round(step["loss"], 4) for step in losses])
+    # Run the research here, with no engine involved. Not the node bodies: they
+    # save and load artifacts, and there is nothing to save to out here. The
+    # functions they wrap are ordinary Python, which is why they are worth
+    # keeping separate — the arithmetic stays yours to run by hand.
+    data = make_rows(limit=64)
+    print("rows:", len(data["rows"]))
+    print("losses:", [round(loss, 4) for loss in train_curve(0.05, 5)])
+    print("score:", score(0.05, 5))