%1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    %2 = stablehlo.add %1, %arg2 : tensor<5xf32>
    %3 = "tfl.abs"(%2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    "stablehlo.return"(%3) : (tensor<5xf32>) -> ()
  }) {dimensions = array<i64: 0>} : (tensor<7x5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0: tensor<5xf32>
}

// -----

    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    %var_0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    %cst_1 = "tf.Const"() {value = dense<2.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>

// CHECK: return %[[DEQUANTIZE]] : tensor<8xf32>
}

// CHECK-LABEL: fakeQuantNotFolded
func.func @fakeQuantNotFolded(tensor<8xf32>, tensor<f32>, tensor<f32>) -> (tensor<8xf32>) {
^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

  %2 = "stablehlo.concatenate"(%0, %1) {dimension = 0 : i64} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
  %3 = "quantfork.stats"(%2) {layerStats = dense<[-0.83811146, 2.4960899]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>
  %4 = "quantfork.stats"(%arg2) {layerStats = dense<[-1.5726943, 1.07351148]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>

      %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)
      %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
      tf_executor.fetch
    }
    func.return
  }

    "tf.AssignVariableOp"(%2, %arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>, tensor<?xf32>) -> ()
    return
  }
}

// -----

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  // CHECK: ml_program.global{{.*}}[[V:@.+]]({{.*}}) : tensor<3xf32>
  // CHECK: func.func @read_inserts_cast_if_necessary(%arg0: tensor<?xf32>
  // CHECK: [[V1:%.*]] = ml_program.global_load

  %4 = "tfr.cast"(%concat) : (!tfr.tensor) -> tensor<3xf32>
  func.return %4 : tensor<3xf32>

// CHECK: %[[concat:.*]] = "tf.RiscConcat"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[concat]]) <{shape = #tf_type.shape<3>}> : (tensor<*xf32>) -> tensor<3xf32>
// CHECK: return %[[es]] : tensor<3xf32>
}

// CHECK-LABEL: decompose_tf_split

  # Note that semantically in Python, multiple return values are equivalent
  # to returning a tuple/list.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}() -> (
  # CHECK-SAME:   tensor<1xf32> {tf_saved_model.index_path = [0]},
  # CHECK-SAME:   tensor<2xf32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0001_multiple_results_no_punctuation"]
  @tf.function(input_signature=[])

// CHECK: return %[[DEQUANTIZE]] : tensor<8xf32>
}

// CHECK-LABEL: fakeQuantNotFolded
func.func @fakeQuantNotFolded(tensor<8xf32>, tensor<f32>, tensor<f32>) -> (tensor<8xf32>) {
^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

func.func @broadcast_mul0(%arg0: tensor<5x7xf32>, %arg1: tensor<7xf32>) -> tensor<5x7xf32> {
  %cst = arith.constant dense<[5, 7]> : tensor<2xi32>
  %0 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<7xf32>, tensor<2xi32>) -> tensor<5x7xf32>
  %1 = "tf.Mul"(%arg0, %0) : (tensor<5x7xf32>, tensor<5x7xf32>) -> tensor<5x7xf32>
  func.return %1 : tensor<5x7xf32>
  // CHECK: %[[V0:.*]] = "tf.Mul"(%arg0, %arg1) : (tensor<5x7xf32>, tensor<7xf32>) -> tensor<5x7xf32>