// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }
// CHECK-EMPTY:

^bb0(%arg0: tensor<4 x f32>, %arg1: tensor<4 x f32>, %arg2: tensor<4 x f32>, %arg3: tensor<4 x f32>):
  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32> loc("Const")

// -----

// CHECK-LABEL: tensorlistSetItem
func.func @tensorlistSetItem(%arg0: tensor<3x10xf32>, %arg1: tensor<1xi32>, %arg2: tensor<i32>, %arg3: tensor<10xf32>) -> tensor<3x10xf32> {
  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<3x10xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<10xf32>>>

  }
// CHECK: func private @mul1(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> attributes {tf.entry_function = {inputs = "mul1_y:0,mul1_x:0", outputs = "PartitionedCall:0"}}
// CHECK:   %[[MUL_0:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_0]] : tensor<1xf32>
// CHECK: }

func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"
      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,

module {
  func.func @main(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>, %arg2: tensor<100xf32>, %arg3: tensor<100xf32>) -> tensor<2x100xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<100xf32>, tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"
      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,

  %0 = "tfl.sub"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %1 = tfl.add %arg1, %arg1 {fused_activation_function = "NONE"} : tensor<*xf32>
  func.return %0, %1 : tensor<*xi32>, tensor<*xf32>
}

func.func @main(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0:2 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):

func.func @replicate_with_packed_input(%arg0: tensor<i1>, %arg1: tensor<i1>) {
  %0:4 = tf_executor.graph {
    %1:5 = tf_executor.island {
      %2:4 = tf_device.replicate(%arg0 as %arg2: tensor<i1>, %arg1 as %arg3: tensor<i1>)
          {n = 2 : i32, _packed_input_indices = [0, 1]} {
        %3 = "tf.opA"(%arg2) : (tensor<i1>) -> tensor<f32>
        %4 = "tf.opB"(%arg3) : (tensor<i1>) -> tensor<i32>

// CHECK-SAME:  ([[ARG0:%.*]]: tensor<i1>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<f32>)
func.func @if_true_branch(%arg0: tensor<i1>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  // CHECK: [[INIT_TOKEN:%.*]] = mhlo.create_token

  // CHECK: [[IF:%.*]]:2 = "mhlo.if"([[ARG0]])
  %0 = "mhlo.if"(%arg0) ({
    // CHECK:      [[TRUE_SEND_TOKEN:%.*]] = "mhlo.send"([[ARG1]], [[INIT_TOKEN]])

// CHECK: %[[dw:.*]] = "tfl.depthwise_conv_2d"(%arg0, %arg1, %arg2)
// CHECK: %[[q:.*]] = "tfl.quantize"(%[[dw]])
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
// CHECK: return %[[dq]], %[[dq]]
}

// Series of values needing requantization -- first the args then the results
// of concatenation operations. concat(concat(arg2, arg0), concat(arg1, arg0)),
// concat(concat(arg2, arg0), arg3)). arg0 should be requantized twice --