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>

            "class OnField { @B(A.CST) String foo; }",
            "class OnParameter { void foo(@B(A.CST) int x) {} }",
            "class InMethodBody { void foo(int x) { @B(A.CST) int value = 5; } }",
        ]

        impl.snapshot { run("impl:${language.compileTaskName}") }

        when:
        source api: ["class A { public static final int CST = 1234; void blah() { /* avoid flakiness by changing compiled file length*/ } }"]

  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,
}

// CHECK-LABEL: @broadcast_both_operand
func.func @broadcast_both_operand(%arg0: tensor<7xf32>, %arg1: tensor<5x1xf32>) -> tensor<5x7xf32> {
  %cst = arith.constant dense<[5, 7]> : tensor<2xi64>
  %0 = "tf.BroadcastTo"(%arg0, %cst) : (tensor<7xf32>, tensor<2xi64>) -> tensor<5x7xf32>
  %1 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<5x1xf32>, tensor<2xi64>) -> tensor<5x7xf32>
  %2 = "tf.Add"(%0, %1) : (tensor<5x7xf32>, tensor<5x7xf32>) -> tensor<5x7xf32>

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 private @simple_callee() -> tensor<2xi32>  {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>
}

func.func private @simple_callee_with_noinline() -> tensor<2xi32> attributes {tf._noinline = true} {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>
}

// Test that simple TF operations can be inlined.

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",

  %cst = arith.constant dense<3> : tensor<1xi32>
  %0 = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<?x3xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<3xf32>>>
  %1 = "tf.TensorListStack"(%0, %cst) {num_elements = 2 : i64} : (tensor<!tf_type.variant<tensor<3xf32>>>, tensor<1xi32>) -> tensor<2x3xf32>
  func.return %1 : tensor<2x3xf32>

// CHECK:  [[ELEM_SHAPE:%cst.*]] = arith.constant dense<3> : tensor<1xi32>

func.func @conv2d_unmatching_loc_pattern(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x3x2x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense_resource<__elided__> : tensor<2x3x3x2xbf16>} : () -> tensor<2x3x3x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>
  %1 = "tf.Conv2D"(%0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1]}