module {
  func.func @not_propagate_matmul(%arg0: tensor<1x2x2x2xf32>) -> tensor<*xf32> {
    %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8>
    %cst_0 = "tf.Const"() {value = dense<0.0157480314> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
    %1 = "tf.Cast"(%0) {Truncate = false} : (tensor<2x1024xi8>) -> tensor<2x1024xf32>

  %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>

// CHECK-LABEL: func.func @consecutive_add_add

// CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<-30> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %[[ADD:.*]] = "tf.AddV2"(%arg0, %[[CST]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK: return %[[ADD]] : tensor<i32>
}

func.func @consecutive_add_sub(%arg0: tensor<i32>) -> (tensor<i32>) {
  %cst = "tf.Const"() {value = dense<-18> : tensor<i32>} : () -> tensor<i32>

// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[matmul]])
// CHECK: return %[[identity]] : tensor<1x2xf32>

func.func @cast_bf16_depthwise_conv_to_fp32(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x2x2x6xf32>) {

func.func @init_all_tables() {
  %cst = arith.constant dense<"%FILE_PLACEHOLDER"> : tensor<!tf_type.string>
  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_/tmp/vocab.txt_-2_-1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

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*/ } }"]

// 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