"application/vnd.fujitsu.oasys2",
				"application/vnd.fujitsu.oasys3",
				"application/vnd.fujitsu.oasysgp",
				"application/vnd.fujitsu.oasysprs",
				"application/vnd.fujixerox.art-ex",
				"application/vnd.fujixerox.art4",
				"application/vnd.fujixerox.hbpl",
				"application/vnd.fujixerox.ddd",
				"application/vnd.fujixerox.docuworks",
				"application/vnd.fujixerox.docuworks.binder",

  %0 = "tf.Less"(%arg0, %arg4) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<*xi1>

// CHECK:  %[[BATCHNORM1_a:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[ADD2]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  %[[BATCHNORM1_b:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[BATCHNORM1_a]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  "tf.FusedBatchNormV3"(%[[BATCHNORM1_b]], %[[ARG1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
}


func.func @batchNormWithGlobalNormalization(

func.func @decompose_resource_apply_adam_non_nesterov(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>, %arg3: tensor<f32>, %arg4: tensor<f32>, %arg5: tensor<f32>, %arg6: tensor<f32>) -> () {
  "tf_device.cluster"() ({

    // CHECK-DAG: [[ONE:%.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}>

// CHECK:     %2 = "tfl.range"(%arg3, %1, %arg4) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<?xi32>
// CHECK:     %3 = "tfl.pack"(%1, %arg4) <{axis = 0 : i32, values_count = 2 : i32}> {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>

  "tf.opB"(%3) {_xla_compile_device_type = "TPU", _replication_info = "replicate", device = "/device:TPU:0", name = "name", is_stateless = true} : (tensor<i1>) -> ()
  %4 = "tf.TPUReplicatedInput"(%arg4, %arg4) {index = 2 : i64} : (tensor<i1>, tensor<i1>) -> tensor<i1>
  "tf.opC"(%4) {_xla_compile_device_type = "TPU", _replication_info = "replicate", device = "/device:TPU:0", name = "name", is_stateless = true} : (tensor<i1>) -> ()

    return %3 : tensor<1x3x2x2xf32>
  }
  func.func private @quantized_conv2d_with_bias_and_relu_fn_0(%arg0: tensor<1x3x4x3xi8>, %arg1: tensor<2x3x3x2xi8>, %arg2: tensor<2xi32>, %arg3: tensor<f32>, %arg4: tensor<i32>, %arg5: tensor<f32>, %arg6: tensor<i32>, %arg7: tensor<f32>, %arg8: tensor<i32>, %arg9: tensor<f32>, %arg10: tensor<i32>) -> tensor<1x3x2x2xi8> {
    %cst = "tf.Const"() {value = dense<127> : tensor<i32>} : () -> tensor<i32>

		err = errnoErr(e1)
	}
	return
}

// THIS FILE IS GENERATED BY THE COMMAND AT THE TOP; DO NOT EDIT

func KeyctlInt(cmd int, arg2 int, arg3 int, arg4 int, arg5 int) (ret int, err error) {
	r0, _, e1 := Syscall6(SYS_KEYCTL, uintptr(cmd), uintptr(arg2), uintptr(arg3), uintptr(arg4), uintptr(arg5), 0)
	ret = int(r0)
	if e1 != 0 {
		err = errnoErr(e1)
	}
	return
}

}

// THIS FILE IS GENERATED BY THE COMMAND AT THE TOP; DO NOT EDIT

func impl_Prctl(option int, arg2 uintptr, arg3 uintptr, arg4 uintptr, arg5 uintptr) (err error) {
	runtime.EnterSyscall()
	r0, e2, e1 := CallLeFuncWithErr(GetZosLibVec()+SYS___PRCTL_A<<4, uintptr(option), uintptr(arg2), uintptr(arg3), uintptr(arg4), uintptr(arg5))
	runtime.ExitSyscall()
	if int64(r0) == -1 {
		err = errnoErr2(e1, e2)
	}
	return
}

// non-negative integer that is returned by the prctl syscall.
func PrctlRetInt(option int, arg2 uintptr, arg3 uintptr, arg4 uintptr, arg5 uintptr) (int, error) {
	ret, _, err := Syscall6(SYS_PRCTL, uintptr(option), uintptr(arg2), uintptr(arg3), uintptr(arg4), uintptr(arg5), 0)
	if err != 0 {
		return 0, err
	}
	return int(ret), nil
}

func Setuid(uid int) (err error) {