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

  %8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xf16>, tensor<4xf16>) -> tensor<4xf16>

  func.return %5, %6, %7, %8 : tensor<f16>, tensor<4xf16>, tensor<4xf16>, tensor<4xf16>
}

// CHECK-LABEL: @elementwise_unary_ops
func.func @elementwise_unary_ops() -> (tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>) {

}

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> {

  %1 = "tfl.pseudo_const" () {value = dense<false> : tensor<4xi1>} : () -> tensor<4xi1> loc("Const2")
  %2 = "tfl.logical_or"(%arg0, %1) : (tensor<4xi1>, tensor<4xi1>) -> tensor<4xi1> loc("logical_or")
  %3 = "tfl.logical_and"(%2, %0) : (tensor<4xi1>, tensor<4xi1>) -> tensor<4xi1> loc("logical_and")
  func.return %3 : tensor<4xi1>

// 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
  }

}

// -----

// If with a 4xi1 condition.

func.func private @testIf1Then(tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
func.func private @testIf1Else(tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

// CHECK-LABEL: func @testIf1x4
func.func @testIf1x4(tensor<4xi1>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> {
^bb0(%arg0: tensor<4xi1>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>):

    value {
      type: DT_BOOL
    }
  }
}

# CHECK-DAG: tf.IteratorGetNext{{.+}}-> (tensor<1x8xbf16>, tensor<2x?x16xf32>, tensor<*xf64>)
# CHECK-DAG: tf.IteratorGetNextSync{{.+}}-> (tensor<*xi16>, tensor<3x24xi32>, tensor<?x4x32xi64>)
# CHECK-DAG: tf.MultiDeviceIteratorGetNextFromShard{{.+}}-> (tensor<5x40xf16>, tensor<*xcomplex<f32>>, tensor<6x48x?xcomplex<f64>>)

  // CHECK: %[[REDUCED:.*]] = mhlo.reduce(%[[CAST]] init: %[[INITIAL]]) applies mhlo.maximum across dimensions = [1] : (tensor<4x8xf16>, tensor<f16>) -> tensor<4xf16>
  // CHECK: %[[CAST_BACK:.*]] = mhlo.convert %[[REDUCED]] : tensor<4xf16>
  // CHECK: %[[RESULT:.*]] = mhlo.reshape %[[CAST_BACK]] : (tensor<4xf16>) -> tensor<4x1xf16>
  // CHECK: return %[[RESULT]] : tensor<4x1xf16>