def METHOD_NAME(model_path, verbose=False):
    import sleap
    import numpy as np
    print("=====")
    print("Model:", model_path)
    print("=====")
    rel_path = lambda x: os.path.join(model_path, x)
    initial_cfg = sleap.load_config(rel_path("initial_config.json"))
    cfg = sleap.load_config(rel_path("training_config.json"))
    print("=====")
    print("Heads:")
    print("=====")
    print(cfg.model.heads)
    print("=====")
    print()
    print("=====")
    print("Backbone:")
    print("=====")
    print(cfg.model.backbone)
    print("=====")
    print()
    print()
    def describe_metrics(metrics):
        if isinstance(metrics, str):
            metrics = np.load(metrics, allow_pickle=True)["metrics"].tolist()
        print(
            f"Dist (90%/95%/99%): {metrics['dist.p90']} / {metrics['dist.p95']} / {metrics['dist.p99']}"
        )
        print(
            f"OKS VOC (mAP / mAR): {metrics['oks_voc.mAP']} / {metrics['oks_voc.mAR']}"
        )
        print(
            f"PCK (mean {metrics['pck.thresholds'][0]}-{metrics['pck.thresholds'][-1]} px): {metrics['pck.mPCK']}"
        )
    def describe_dataset(split_name):
        if os.path.exists(rel_path(f"labels_gt.{split_name}.slp")):
            labels = sleap.load_file(rel_path(f"labels_gt.{split_name}.slp"))
            print(
                f"Frames: {len(labels.user_labeled_frames)} / Instances: {len(labels.user_instances)}"
            )
        if os.path.exists(rel_path(f"metrics.{split_name}.npz")):
            print("Metrics:")
            describe_metrics(rel_path(f"metrics.{split_name}.npz"))
    print("=====")
    print("Training set:")
    print("=====")
    describe_dataset("train")
    print("=====")
    print()
    print("=====")
    print("Validation set:")
    print("=====")
    describe_dataset("val")
    print("=====")
    print()
    print("=====")
    print("Test set:")
    print("=====")
    describe_dataset("test")
    print("=====")
    print()