}
library {
  function {
    signature {
      name: "cond_true"
      input_arg {
        name: "cond_true"
        type: DT_INT32
      }
      input_arg {
        name: "cond_true1"
        type: DT_INT32
      }
      output_arg {
        name: "cond_true2"
        type: DT_INT32
      }
      output_arg {
        name: "cond_true3"
        type: DT_INT32
      }
    }
    node_def {

  experimental_debug_info {
  }
}
library {
  function {
    signature {
      name: "cond_true"
      input_arg {
        name: "cond_true"
        type: DT_FLOAT
      }
      input_arg {
        name: "cond_true1"
        type: DT_FLOAT
      }
      output_arg {
        name: "cond_true2"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "tf.Add"
      op: "Add"

// CHECK:   %{{.*}} = "tf.If"(%{{.*}}, %{{.*}}, %{{.*}}) <{else_branch = @cond_false, is_stateless = false, then_branch = @cond_true}> : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %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>

# CHECK:      "tf.If"{{.+}}else_branch = @cond_false0{{.+}}is_stateless = false{{.+}}then_branch = @cond_true0
# CHECK:      func private @cond_false_10
# CHECK-NEXT: tfl.div
# CHECK:      func private @cond_true_10
# CHECK-NEXT: tfl.sub
# CHECK:      func private @cond_false0
# CHECK-NEXT: tfl.mul
# CHECK:      func private @cond_true0

    %outputs_6, %control_7 = tf_executor.island wraps "tf.If"(%outputs_2, %arg2, %arg3) {else_branch = @cond_false, is_stateless = true, then_branch = @cond_true} : (tensor<i1>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("StatelessIf")

  %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.func @cond_true(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<*xf32>

  %1 = "tf.Add"(%arg0, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

// CHECK-LABEL: func @cond_true(%arg0: !tfrt.chain, %arg1: !tfrt_fallback.tf_tensor) -> (!tfrt.chain, !tfrt_fallback.tf_tensor)
func.func @cond_true(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<i32>} : () -> tensor<i32>

// -----

// A resource is passed into tf.If
func.func @cond_false(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<f32>) -> tensor<f32> {
  func.return %arg1 : tensor<f32>
}

func.func @cond_true(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

// CHECK:  return [[RESULT]] : tensor<?x10xf32>
}

// CHECK-LABEL:  func private @cond_true

    // Constructs `then_branch`, which is executed when `if_cond` evaluates to
    // true.
    auto then_branch_op =
        rewriter.create<func::FuncOp>(loc, "cond_true", func_type);
    CreateCondTrueBranch(op, shape_dtype, result_type, then_branch_op,
                         &rewriter);
    then_branch_op.setVisibility(func::FuncOp::Visibility::Private);