// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[call:.*]] = "tf.PartitionedCall"(%[[dq2]]
// CHECK-SAME: f = @composite_matmul_with_bias_fn_1
// CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[call]])
// CHECK-SAME: quant.uniform<i8:f32, 0.015686274509803921:-1>

// CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[dq1]]
// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()

	testCases := []struct {
		description     string
		toleration      Toleration
		taint           Taint
		expectTolerated bool
	}{
		{
			description: "toleration and taint have the same key and effect, and operator is Exists, and taint has no value, expect tolerated",
			toleration: Toleration{
				Key:      "foo",
				Operator: TolerationOpExists,
				Effect:   TaintEffectNoSchedule,
			},

   *       the same.
   *   <li>The unreserved characters ".", "-", "~", and "_" remain the same.
   *   <li>The general delimiters "@" and ":" remain the same.
   *   <li>The subdelimiters "!", "$", "&amp;", "'", "(", ")", "*", "+", ",", ";", and "=" remain
   *       the same.
   *   <li>The space character " " is converted into %20.

# Tests single target node with no pruning set. All nodes will remain in the
# graph and the target node is added to the graph fetch as a control.
#
# CHECK-LABEL: func @main
# CHECK-SAME:  control_outputs = "AssignAdd"
# CHECK-SAME:  inputs = ""
# CHECK-SAME:  outputs = ""
# CHECK:           %{{.*}}, %[[ASSIGN_ADD_CTRL:.*]] = tf_executor.island wraps "tf.AssignAdd"
# CHECK:           tf_executor.fetch %[[ASSIGN_ADD_CTRL]]

    return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-SAME:   dim_args_spec = []
    // CHECK-NOT:    _entry_function

// CHECK-LABEL: @init
func.func @init() -> tensor<i32> {
  // CHECK-NOT: tf.VariableV2

  // CHECK: [[zero:%.*]] = "tf.Const"
  // CHECK-SAME: dense<0>
  %0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

  // CHECK: [[handle:%.*]] = "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "x"
  // CHECK-NEXT: "tf.AssignVariableOp"([[handle]], [[zero]])
  // CHECK-NEXT: "tf.ReadVariableOp"([[handle]])

// tf_device.Cluster.

// CHECK-LABEL: func @testReadVariableOpColocated
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)

	netns1 := p.UpsertPodCacheWithNetns(string(pod.UID), wl)
	if !reflect.DeepEqual(netns1, ns) {
		t.Fatalf("Expected the same Netns for the same uid, got %v and %v", netns1, ns)
	}

	ns2 := newFakeNsInode(inc(), 1)
	wl2 := WorkloadInfo{
		Workload: podToWorkload(pod),
		Netns:    ns2,
	}
	// when using same uid, the original netns should be returned
	netns2 := p.UpsertPodCacheWithNetns(string(pod.UID), wl2)
	if netns2 != ns {