"readinessInitialDelaySeconds": 0,
          "readinessPeriodSeconds": 15,
          "resources": {
            "limits": {
              "cpu": "2000m",
              "memory": "1024Mi"
            },
            "requests": {
              "cpu": "100m",
              "memory": "128Mi"
            }
          },
          "startupProbe": {
            "enabled": true,

port: 443 protocol: TCP targetPort: 443 annotations: {} loadBalancerIP: "" loadBalancerSourceRa: [] externalTrafficPolic: "" externalIPs: [] ipFamilyPolicy: "" ipFamilies: [] resources: requests: cpu: 100m memory: 128Mi limits: cpu: 2000m memory: 1024Mi autoscaling: enabled: true minReplicas: 1 maxReplicas: 5 targetCPUUtilization: 80 targetMemoryUtilizat: {} autoscaleBehavior: {} # Pod environment variables env: {} # Labels to apply to all resources labels: {} # Annotations to apply to all resources...

DATA ·IEEEConst+2456(SB)/8,$0x000000019e588d52

	/* x^103488 mod p(x), x^103424 mod p(x) */
DATA ·IEEEConst+2464(SB)/8,$0x00000001415598a0
DATA ·IEEEConst+2472(SB)/8,$0x00000001180f0bbc

	/* x^102464 mod p(x), x^102400 mod p(x) */
DATA ·IEEEConst+2480(SB)/8,$0x00000000bf6c8c90
DATA ·IEEEConst+2488(SB)/8,$0x00000000e1d9177a

	/* x^101440 mod p(x), x^101376 mod p(x) */
DATA ·IEEEConst+2496(SB)/8,$0x00000001788b0504

      batch_sizes: Sequence[int],
      target_opset: quant_opts_pb2.OpSet,
  ):
    lhs_batch_size, rhs_batch_size = batch_sizes
    input_shape = (*lhs_batch_size, 1, 1024)
    filter_shape = (*rhs_batch_size, 1024, 3)
    static_input_shape = [dim if dim is not None else 2 for dim in input_shape]
    model = self._create_matmul_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,