_EVFILT_READ  = -0x1
	_EVFILT_WRITE = -0x2
)

type tforkt struct {
	tf_tcb   unsafe.Pointer
	tf_tid   *int32
	tf_stack uintptr
}

type sigcontext struct {
	sc_gs       uint32
	sc_fs       uint32
	sc_es       uint32
	sc_ds       uint32
	sc_edi      uint32
	sc_esi      uint32
	sc_ebp      uint32
	sc_ebx      uint32
	sc_edx      uint32
	sc_ecx      uint32
	sc_eax      uint32

	{as: AMTVSRWM, a1: C_REG, a6: C_VREG, asmout: type_xscvuqqp, size: 4},
	{as: APADDI, a1: C_REG, a3: C_S34CON, a4: C_U1CON, a6: C_REG, asmout: type_paddi, ispfx: true, size: 8},
	{as: APEXTD, a1: C_REG, a2: C_REG, a6: C_REG, asmout: type_pextd, size: 4},
	{as: APLFS, a1: C_LOREG, a3: C_U1CON, a6: C_FREG, asmout: type_plxssp, ispfx: true, size: 8},
	{as: APLQ, a1: C_LOREG, a3: C_U1CON, a6: C_REGP, asmout: type_plxssp, ispfx: true, size: 8},

		args         config.NodeResourcesBalancedAllocationArgs
		runPreScore  bool
	}{
		{
			// Node1 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Fraction: 0 / 4000 = 0%
			// Memory Fraction: 0 / 10000 = 0%
			// Node1 Score: (1-0) * MaxNodeScore = MaxNodeScore
			// Node2 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Fraction: 0 / 4000 = 0 %
			// Memory Fraction: 0 / 10000 = 0%

	{AADD, C_REG, C_REG, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{AADDV, C_REG, C_REG, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{AAND, C_REG, C_REG, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{ASUB, C_REG, C_NONE, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{ASUBV, C_REG, C_NONE, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{AADD, C_REG, C_NONE, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},
	{AADDV, C_REG, C_NONE, C_NONE, C_REG, C_NONE, 2, 4, 0, 0},

	}
}

func (m scoreMap) append(other scoreMap) {
	for topology, oScores := range other {
		scores := m[topology]
		if scores == nil {
			m[topology] = oScores
			continue
		}
		for k, v := range oScores {
			scores[k] += v
		}
	}
}

func (pl *InterPodAffinity) processExistingPod(
	state *preScoreState,
	existingPod *framework.PodInfo,

	{as: ASUB, a1: C_REG, a2: C_REG, a6: C_REG, type_: 10, size: 4}, /* op r2[,r1],r3 */
	{as: ASUB, a1: C_REG, a6: C_REG, type_: 10, size: 4},
	{as: ASLW, a1: C_REG, a6: C_REG, type_: 6, size: 4},
	{as: ASLW, a1: C_REG, a2: C_REG, a6: C_REG, type_: 6, size: 4},
	{as: ASLD, a1: C_REG, a6: C_REG, type_: 6, size: 4},
	{as: ASLD, a1: C_REG, a2: C_REG, a6: C_REG, type_: 6, size: 4},

  // CHECK-SAME: [1.000000e+00, 2.000000e+00]
  // CHECK-SAME: tensor<1x2xf32>
  // CHECK: %[[FC_RES:.*]] = "tfl.fully_connected"(%arg0, %[[CONST_WEIGHT]]
  // CHECK-SAME: <{fused_activation_function = "NONE", keep_num_dims = true, weights_format = "DEFAULT"}> : (tensor<4x128x2xf32>, tensor<1x2xf32>, none) -> tensor<4x128x1xf32>
  // CHECK-NEXT: return %[[FC_RES]]
}

// CHECK-LABEL: Batchmatmul2FullyconnectedAdjy
// CHECK-NOT: "tfl.batch_matmul"

	{AADDV, C_REG, C_REG, C_REG, 2, 4, 0, sys.MIPS64, 0},
	{AAND, C_REG, C_REG, C_REG, 2, 4, 0, 0, 0},
	{ASUB, C_REG, C_NONE, C_REG, 2, 4, 0, 0, 0},
	{ASUBV, C_REG, C_NONE, C_REG, 2, 4, 0, sys.MIPS64, 0},
	{AADD, C_REG, C_NONE, C_REG, 2, 4, 0, 0, 0},
	{AADDV, C_REG, C_NONE, C_REG, 2, 4, 0, sys.MIPS64, 0},
	{AAND, C_REG, C_NONE, C_REG, 2, 4, 0, 0, 0},
	{ACMOVN, C_REG, C_REG, C_REG, 2, 4, 0, 0, 0},
	{ANEGW, C_REG, C_NONE, C_REG, 2, 4, 0, 0, 0},

		wantErrs       field.ErrorList
		wantStatusCode framework.Code
	}{
		{
			// Node1 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Score: ((4000 - 0) * MaxNodeScore) / 4000 = MaxNodeScore
			// Memory Score: ((10000 - 0) * MaxNodeScore) / 10000 = MaxNodeScore
			// Node1 Score: (100 + 100) / 2 = 100
			// Node2 scores (remaining resources) on 0-MaxNodeScore scale

		wantErrs       field.ErrorList
		wantStatusCode framework.Code
	}{
		{
			// Node1 scores (used resources) on 0-MaxNodeScore scale
			// CPU Score: (0 * MaxNodeScore)  / 4000 = 0
			// Memory Score: (0 * MaxNodeScore) / 10000 = 0
			// Node1 Score: (0 + 0) / 2 = 0
			// Node2 scores (used resources) on 0-MaxNodeScore scale
			// CPU Score: (0 * MaxNodeScore) / 4000 = 0