}
  function {
    signature {
      name: "cond_false"
      input_arg {
        name: "cond_false"
        type: DT_INT32
      }
      input_arg {
        name: "cond_false1"
        type: DT_INT32
      }
      output_arg {
        name: "cond_false2"
        type: DT_INT32
      }
      output_arg {
        name: "cond_false3"
        type: DT_INT32
      }
    }

      value: "tf.Add:z:0"
    }
  }
  function {
    signature {
      name: "cond_false"
      input_arg {
        name: "cond_false"
        type: DT_FLOAT
      }
      input_arg {
        name: "cond_false1"
        type: DT_FLOAT
      }
      output_arg {
        name: "cond_false2"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "tf.Mul"

// 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>

# 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_4, %control_5 = tf_executor.island wraps "tf.If"(%outputs_2, %arg2, %arg3) {else_branch = @cond_false, is_stateless = false, then_branch = @cond_true} : (tensor<i1>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("StatefulIf")

// CHECK-NEXT:       builtin_options_type: MulOptions,
// CHECK-NEXT:       builtin_options: {
// CHECK-EMPTY:
// CHECK-NEXT:       }
// CHECK-NEXT:     } ],
// CHECK-NEXT:     name: "cond_false"
// CHECK-NEXT:   } ],
// CHECK-NEXT:   description: "MLIR Converted.",
// CHECK-NEXT:   buffers: [ {
// CHECK-EMPTY:
// CHECK-NEXT:   }, {
// CHECK-EMPTY:
// CHECK-NEXT:   }, {
// CHECK-EMPTY:

// RUN: tf-tfrt-opt -tf-to-tfrt="enable-while-parallel-iterations=true" %s | FileCheck %s --dump-input=fail

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

  %7 = "tf.Pack"(%2, %6) : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
  func.return %7 : tensor<2xf32>
}

// -----

// 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>
}

// CHECK:  [[SHAPE_1:%.*]] = "tf.Shape"([[INPUT]]) : (tensor<3x10xf32>) -> tensor<?xi32>
// CHECK:  [[RESULT:%.*]] = "tf.If"([[DIFF_RES]], [[INPUT]], [[SHAPE_1]], [[SIZE_DIFF]], [[SIZE]]) <{else_branch = @cond_false, is_stateless = true, then_branch = @cond_true}> : (tensor<i1>, tensor<3x10xf32>, tensor<?xi32>, tensor<i32>, tensor<i32>) -> tensor<?x10xf32>
// CHECK:  return [[RESULT]] : tensor<?x10xf32>
}

    // Constructs `else_branch`, which is executed when `if_cond` evaluates to
    // false.
    auto else_branch_op =
        rewriter.create<func::FuncOp>(loc, "cond_false", func_type);
    CreateCondFalseBranch(loc, shape_dtype, result_type, else_branch_op,
                          &rewriter);
    else_branch_op.setVisibility(func::FuncOp::Visibility::Private);