}
}

// CHECK-LABEL: HloModule main

  %1 = stablehlo.constant dense<0x7FC00000> : tensor<f32>
  %2 = stablehlo.constant dense<6.000000e+00> : tensor<f32>
  %3 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
  %4 = "stablehlo.add"(%0, %1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %5 = stablehlo.clamp %3, %arg0, %2 : (tensor<f32>, tensor<?x112x112x64xf32>, tensor<f32>) -> tensor<?x112x112x64xf32>
  %6 = "stablehlo.reduce_window"(%5, %4) ({

//  Proto debug string (empty but would equivalent to):
//   type: REPLICATED

// CHECK-LABEL: HloModule main
// CHECK:       ENTRY %main.{{[0-9]+}}
// CHECK-SAME:  (arg_tuple.{{[0-9]+}}: (f32[128,10], f32[10,1024], f32[128,1024])) -> (f32[128,10], f32[10,1024], f32[128,1024]) {
// CHECK:         ROOT %tuple.{{[0-9]+}}
// CHECK-SAME:    sharding={
// CHECK-SAME:    {devices=[1,2]0,1}
// CHECK-SAME:    {maximal device=0}
// CHECK-SAME:    {replicated}

// CHECK:         %[[ARG_TUPLE]] = (f32[10,19]{1,0}, f32[19,10]{1,0}) parameter(0), parameter_replication={false,true}
// CHECK:         [[ARG0:%.*]] = f32[10,19]{1,0} get-tuple-element((f32[10,19]{1,0}, f32[19,10]{1,0}) %[[ARG_TUPLE]]), index=0
// CHECK:         [[ARG1:%.*]] = f32[19,10]{1,0} get-tuple-element((f32[10,19]{1,0}, f32[19,10]{1,0}) %[[ARG_TUPLE]]), index=1

  %arg0: tensor<!tf_type.resource<tensor<f32>>>,
  %arg1: tensor<!tf_type.resource<tensor<f32>>>,
  %arg2: tensor<f32>) -> () {
  tf_executor.graph {
    // expected-warning@+1 {{unexpected control dependency path: path 0, node 0 (source)}}
    %island1 = tf_executor.island {
      "tf.AssignVariableOp"(%arg0, %arg2) : (tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()
      tf_executor.yield
    }

func.func @duplicate_vars_with_users(%arg0: tensor<f32>) {
  %0 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>
  %1 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
  %2 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>

// 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]])

!tf_res_f32 = tensor<*x!tf_type.resource<tensor<f32>>>

// CHECK-LABEL: func @hoist_tail_assign
// CHECK-SAME:  ([[ARG0:%.*]]: tensor<*x!tf_type.resource<tensor<f32>>>)
func.func @hoist_tail_assign(%arg0: !tf_res_f32) {
  // CHECK: [[REPLICATE:%.*]]:4 = tf_device.replicate {n = 2 : i32}
  %replicate:2 = tf_device.replicate {n = 2 : i32} {
    // CHECK: [[OP_A:%.*]] = "tf.OpA"
    %op_a = "tf.OpA"() : () -> tensor<f32>
    // CHECK: [[OP_B:%.*]] = "tf.OpB"

  %1:4 = "tf.CustomAggregator"(%arg0) <{id = "1", calibration_method = 1 : i32, num_bins = 0 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> {min = 7.547870e-07 : f32, max = 0.999992311 : f32} : (tensor<1x1024xf32>) -> (tensor<1x1024xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>)

  func.func @f(
    %arg0: tensor<f32> {tf.resource_name = "resource"}
  ) attributes { tf_saved_model.exported_names = ["foo.some_func"] } {
    func.return
  }

}

// -----

module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.0> : tensor<f32> } : () -> ()
  // CHECK: func @f
  func.func @f(