coding implementation

okay = RUN_KNOBS train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT, “–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN, *COMMON, “train.train_size=0″, f”train.num_epochs={k[‘num_epochs’]}”, f”prepare.batch_size={okay[‘batch_size’]}”, f”gradient.minibatch_size={okay[‘minibatch’]}”, f”gradient.merge_batch_size={okay[‘merge_batch’]}”, f”gradient.analyst_workers={okay[‘workers’]}”, f”optimizer.learning_rate={okay[‘lr’]}”, f”optimizer.lr_scheduler={okay[‘lr_sched’]}”, “optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”, f”env.employees={okay[‘workers’]}”, f”env.restrict={okay[‘limit’]}”], “TRAIN (rollout->reflect->aggregate->select->update->gate; slow-update + meta-skill)”) import pandas as pd, matplotlib.pyplot as plt hist = json.hundreds(pathlib.Path(f”{RUN}/historical past.json”).read_text()) df = pd.json_normalize(hist) print(“nhistory.json columns:”, listing(df.columns)) def col(*cands): for c in cands: for precise in df.columns: if c in…