}

// CHECK-LABEL: func @conv_with_single_layer

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]]
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

    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>>>)

  func.return
}

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[ARG_0]], %[[ARG_0]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_2]], %[[ARG_2]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_4]], %[[ARG_4]]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}

	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 {

// CHECK: return %[[CALL]]

// CHECK: quantized_dot_general_fn
// CHECK-SAME: (%[[ARG1:.+]]: tensor<1x2xf32>,  %[[ARG2:.+]]: tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>
// CHECK: %[[DOT:.+]] = stablehlo.dot_general %[[ARG1]], %[[ARG2]]
// CHECK-SAME: (tensor<1x2xf32>, tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>

// CHECK: func.func @init_func_restore_op
// CHECK-SAME: tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"]
// CHECK-SAME: tf_saved_model.initializer_type = "restore_op"

// Test that a new save function that wraps the SaveV2 op is created.
// CHECK: func.func private @tf_quant__save(%[[ARG:.*]]: tensor<!tf_type.string>)
// CHECK: %[[VAR_HANDLE_0:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_0".*}}

    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
library {
}
versions {
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<f32>) -> tensor<*xf32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "output_node"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph

func.func @does_reordering_for_nested_compiles() -> () {
   // CHECK: _TPUCompileMlir
   // CHECK-SAME: Z
   // CHECK: tf_device.launch
   // CHECK-NEXT: _TPUCompileMlir
   // CHECK-SAME: X
   // CHECK: tf_device.launch
   // CHECK-NEXT: _TPUCompileMlir
   // CHECK-SAME: Y
   // CHECK: OpA
   // CHECK: OpB
   // CHECK: OpC
   "tf.OpA"() : () -> ()
   "tf_device.launch"() ({