func.return %arg0: tensor<1x!tf_type.qint8>
}

// -----

// CHECK-LABEL: func @id_qint16(%arg0: tensor<1xi16>) -> tensor<1xi16> {
func.func @id_qint16(%arg0: tensor<1x!tf_type.qint16>) -> tensor<1x!tf_type.qint16> {
  // CHECK-NEXT: return %arg0 : tensor<1xi16>
  func.return %arg0: tensor<1x!tf_type.qint16>
}

// -----

// CHECK-LABEL: func @id_qint32(%arg0: tensor<1xi32>) -> tensor<1xi32> {

}

func.func @floor_mod_i16(%arg0: tensor<5xi16>, %arg1: tensor<5xi16>) -> tensor<5xi16> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xi16>, tensor<5xi16>) -> tensor<5xi16>
  func.return %0 : tensor<5xi16>
  // CHECK-LABEL: floor_mod_i16
  // CHECK: "tfl.floor_mod"(%arg0, %arg1) : (tensor<5xi16>, tensor<5xi16>) -> tensor<5xi16>
}

func.func @exp(%arg0: tensor<5xf32>) -> tensor<5xf32> {

func.func @preserve_assert(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi1> {
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1>
  "tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> ()
  func.return %0 : tensor<1xi1>
  // CHECK-LABEL: preserve_assert
  // CHECK: tfl.less_equal
  // CHECK: Assert
  // CHECK: return

  func.return
}

// CHECK-LABEL: @tf_if_gets_control_node
func.func @tf_if_gets_control_node(%arg0: tensor<1xi1>)->() {
 "tf.If"(%arg0) {_lower_using_switch_merge = true, _read_only_resource_inputs = [], device = "", else_branch = @noop, is_stateless = false, output_shapes = [#tf_type.shape<>], then_branch = @noop} : (tensor<1xi1>) -> ()
 func.return
}
// CHECK-NEXT: %[[CONTROL:.*]] = tfl.control_node controls "tf.If"
// CHECK-NEXT: return

  %0 = "tfl.less"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1>
  %1 = "tf.If"(%0, %arg0, %arg1) {else_branch = @cond_false, then_branch = @cond_true, is_stateless = false} : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %1 : tensor<1xf32>
}

  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16
  // CHECK: value = dense<[1, 2, 3, 258]> : tensor<4xi16>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 258]> : tensor<4xi16> } : () -> tensor<4xi16>
  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32

  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16
  // CHECK: value = dense<[1, 2, 3, 258]> : tensor<4xi16>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 258]> : tensor<4xi16> } : () -> tensor<4xi16>
  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32

    }
  }
}
versions {
  producer: 216
}

# NONE: func @main(%arg0: tensor<i1>, %arg1: tensor<1xi32>) -> (tensor<i1>, tensor<1xi32>)

// CHECK:            shape {
// CHECK-NEXT:         unknown_rank: true


func.func @main() {
  tf_executor.graph {
    %0:4 = tf_executor.island wraps "tf.InfeedDequeueTuple"() : () -> (tensor<3xi32>, tensor<4x?xf32>, tensor<*xi16>)
    tf_executor.fetch
  }
  func.return

  %8 = "tf.opI"() : () -> tensor<*xf32>
  %9 = "tf.opJ"() : () -> tensor<*xi8>
  %10 = "tf.opK"() : () -> tensor<*xi16>
  %11 = "tf.opL"() : () -> tensor<*xi64>
  tf_device.replicate([%0, %1, %2] as %input0: tensor<*xi1>, %9 as %input1: tensor<*xi8>, %10 as %input2: tensor<*xi16>, [%3, %4, %5] as %input3: tensor<*xi32>, [%6, %7, %8] as %input4: tensor<*xf32>, %11 as %input5: tensor<*xi64>) {n = 3 : i32} {
    tf_device.return
  }