// CHECK:           return %[[VAL_3]] : tensor<123xf32>
// CHECK:         }
func.func @convert_iota_1d() -> tensor<123xf32> {
  %0 = "mhlo.iota"() <{ iota_dimension = 0 : i64 }> : () -> tensor<123xf32>
  func.return %0 : tensor<123xf32>
}

// CHECK-LABEL:   func @convert_iota_3d() -> tensor<5x7x9xi32> {

  %1 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> 
  %2 = "tf.AddV2"(%0, %1) {device = "/device:CPU:0"} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  %3 = "tf.Identity"(%2) {device = "/device:CPU:0"} : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %3 : tensor<1x3xf32>
}

      Targs = [tensor<32xf32>, tensor<64xf32>, tensor<16xf32>],
      Tresults = [tensor<32xf32>, tensor<16xf32>]}
      : (tensor<32xf32>, tensor<64xf32>, tensor<16xf32>, tensor<2x!tf_type.string>) -> (tensor<32xf32>, tensor<16xf32>)
    tf_device.return %0#0, %0#1 : tensor<32xf32>, tensor<16xf32>
  }) {device = "/job:localhost/replica:0/task:0/device:TPU:0"} : () -> (tensor<32xf32>, tensor<16xf32>)
  // CHECK-NEXT: tf_device.return
  // CHECK-NEXT: })

      %outputs_0, %control_1 = tf_executor.island(%control) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32>
      %outputs_2, %control_3 = tf_executor.island(%control_1) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<5x5x32x16xf32>} : () -> tensor<5x5x32x16xf32>

// CHECK:           [[VAL_5:%.*]] = call @func_1_GPU_FLOAT([[VAL_0]], [[VAL_3]]) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

func.func @foo(%arg0: tensor<1x4xf32>, %arg1: tensor<1x4xf32>) -> tensor<1x4xf32> {
  func.return %arg0 : tensor<1x4xf32>
}

func.func @bar(%arg0: tensor<2x4xf32>, %arg1: tensor<2x4xf32>) -> tensor<2x4xf32> {
  func.return %arg0 : tensor<2x4xf32>
}

func.func @foobar(%arg0: tensor<1x4xf32>, %arg1: tensor<1x4xf32>) -> tensor<1x4xf32> {
  func.return %arg0 : tensor<1x4xf32>
}

// CHECK-DAG: func @main

// -----

func.func @testAddReluPack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) {
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

  func.return %0 : tensor<256x32x32x16xf32>
}

func.func @testConv2DDynamicShape(tensor<?x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32> {
^bb0(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: _arithmetic_count = -1 : i64

  func.return %1 : tensor<i32>
}

func.func private @xla_func_0(%arg0: tensor<1x3xf32>, %arg1: tensor<1x3xf32>) -> tensor<1x3xf32> attributes {tf._XlaMustCompile = true, tf._noinline = true, tf._original_func_name = "should_not_be_used"} {
  %1 = "tf.AddV2"(%arg0, %arg1) : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  %2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

// CHECK-LABEL: func @testLeakyRelu(%arg0: tensor<16xf32>)
func.func @testLeakyRelu(tensor<16xf32>) -> tensor<16xf32> {
^bb0(%arg0: tensor<16xf32>):
  %0 = "tf.LeakyRelu"(%arg0) {alpha = 0.2 : f32} : (tensor<16xf32>) -> tensor<16xf32>
  func.return %0 : tensor<16xf32>
}

// -----
func.func @testLeakyWrongAlphaType(tensor<16xf32>) -> tensor<16xf32> {
^bb0(%arg0: tensor<16xf32>):