%0 = "tfl.pseudo_const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
   %1 = "tfl.mean"(%arg0, %0) {keep_dims = false} : (tensor<?x7x7x2048x!quant.uniform<i8:f32, 0.6:-128>>, tensor<2xi32>) -> tensor<?x2048x!quant.uniform<i8:f32, 0.6:-128>>
  func.return %1 : tensor<?x2048x!quant.uniform<i8:f32, 0.6:-128>>
}

  }
}

// CHECK-LABEL: HloModule main

// CHECK: %[[id2:.*]] = "tf.RiscSame"(%arg2) : (tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[id2]]) <{shape = #tf_type.shape<>}> : (tensor<*xf32>) -> tensor<f32>
// CHECK: return %[[es]] : tensor<f32>
}

// CHECK-LABEL: decompose_tf_concat_n
func.func @decompose_tf_concat_n(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<3xf32> {
  %0 = "tfr.cast"(%arg0) : (tensor<f32>) -> !tfr.tensor

  %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

  // CHECK: "tfl.unidirectional_sequence_lstm"(%arg0,...

// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w]]) : (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<64x3x3x3xf32>
// CHECK: %[[emb:.*]] = "tfl.gather"(%[[dq_w]], %arg0)
// CHECK: return %[[emb:.*]]

// PerTensor: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

  %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>>>