%cst = arith.constant dense<[false, true]> : tensor<2xi1>
  %0 = "tfl.cast"(%cst) : (tensor<2xi1>) -> tensor<2xi8>
  func.return %0 : tensor<2xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, 1]> : tensor<2xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i1_to_ui8
func.func @cast_i1_to_ui8() -> tensor<2xui8> {
  %cst = arith.constant dense<[false, true]> : tensor<2xi1>

  func.return %0 : tensor<3x5x4xf32>
  // CHECK-LABEL: unary_einsum_reduce_sum_transpose
  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<3> : tensor<1xi32>
  // CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[0, 2, 1]> : tensor<3xi32>
  // CHECK: %[[v0:.*]] = "tf.Sum"(%arg0, %[[cst]]) <{keep_dims = false}> : (tensor<3x4x5x6xf32>, tensor<1xi32>) -> tensor<3x4x5xf32>

  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %cst = arith.constant dense<1.0> : tensor<5x2x3x4xf32>
  %1 = tfl.add %0, %cst { fused_activation_function = "NONE" } : tensor<5x2x3x4xf32>
  func.return %1 : tensor<5x2x3x4xf32>
}

// CHECK: %cst = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
// CHECK: %cst_0 = arith.constant dense<1.000000e+00> : tensor<2x3x4x5xf32>

func.func @NotQuantizeNoneType() -> none {
  %cst = "tfl.no_value"() {value = unit} : () -> none
  func.return %cst : none

// CHECK-NEXT:  %[[cst:.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK-NEXT:  return %[[cst]]
}

// CHECK-LABEL: QuantizeZeroSplat
func.func @QuantizeZeroSplat() -> tensor<2x3xf32> {
  %cst = arith.constant dense<0.0> : tensor<2x3xf32>
  func.return %cst : tensor<2x3xf32>

module {
  func.func @restore_single() {
    %cst = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
    %cst_0 = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>
    %cst_1 = "tf.Const"() <{value = dense<"y"> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>

func.func @hashtable_string_to_int64(%arg0: tensor<i64>) -> tensor<*xi64> {
  %cst = arith.constant dense<"f"> : tensor<!tf_type.string>
  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_e308c10b-91c8-416c-81f9-af5bf6aba847", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  func.return %3 : tensor<40x40xf32>

  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<5.000000e+00> : tensor<40x40xf32>
  // CHECK: %[[fc:.*]] = "tfl.fully_connected"(%arg0, %arg1, %[[cst]]) <{asymmetric_quantize_inputs = true,

  // FOLD: %[[cst:.*]] = arith.constant dense<5.000000e+00> : tensor<40x40xf32>
  // FOLD: %[[fc:.*]] = "tfl.fully_connected"(%arg0, %arg1, %[[cst]]) <{asymmetric_quantize_inputs = true,
}

func.func @prune_unused_restore() {
  %cst = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
  %cst_0 = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>
  %cst_1 = "tf.Const"() <{value = dense<"y"> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>
  // CHECK-NOT: tf.RestoreV2

func.func @qdq_for_conv_weight_empty(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {
    Sout = [#tf_type.shape<1x2x2x2>], _entry_function = @composite_conv_fn,
    _original_entry_function = "composite_conv_fn",

func.func @decompose_batch_norm(%arg0: tensor<*xf32>) -> (tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %cst_0 = "tf.Const"() {value = dense<0.500000e+00> : tensor<2xf32>} : () -> tensor<2xf32>