// 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>) {
  %cst = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<2x3x3x2xbf16>} : () -> tensor<2x3x3x2xbf16>

  func.func @main(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    %0 = "tf.Identity"(%arg0) : (tensor<?xi32>) -> tensor<?xi32> loc(unknown)
    func.return %0 : tensor<?xi32> loc(unknown)
  } loc(unknown)
} loc(unknown)

// Tests printer for tf_executor.island "wraps" short form.

// CHECK-LABEL: func @island_wrap_print
func.func @island_wrap_print(%arg0: tensor<i32>, %arg1: tensor<f32>) {
  tf_executor.graph {
    // CHECK: tf_executor.island wraps "tf.IdentityN"
    %0:3 = tf_executor.island {
      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")

TEST_F(SaveQuantizationReportInstrumentationTest, SaveReport) {
  constexpr absl::string_view kModuleWithCompositeDotGeneral = R"mlir(
    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
      %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
      %0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>

  // CHECK: func @f(%arg0
  func.func @f(%arg0: tensor<!tf_type.string> {tf_saved_model.index_path = [0]})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

func.func @xla_dot_v2(%arg0: tensor<?x2x3xf32>, %arg1: tensor<3x4x5xf32>) -> (tensor<?x2x4x5xf32>) {
  %0 = "tf.XlaDotV2"(%arg0, %arg1) {device = "", dimension_numbers = "\0A\01\02\12\01\00", precision_config = ""} : (tensor<?x2x3xf32>, tensor<3x4x5xf32>) -> tensor<?x2x4x5xf32>
  func.return %0 : tensor<?x2x4x5xf32>
}

// CHECK: func @xla_dot_v2

// CHECK-NEXT:    [[TENSOR:%.*]] = "tf_mlrt.tf_await"([[FURTURE]]) : (!mlrt.future) -> tensor<3x1xf32>
// CHECK-NEXT:    "tf.MatMul"(%arg0, [[TENSOR]]) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    "tf.IfrtCall"(%arg0, [[ARRAYKEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> {__tpu_compile_metadata_text = "retvals { sharding { } }"} : (tensor<1x3xf32>, tensor<!tf_type.string>) -> tensor<1x1xf32>

}

// CHECK-LABEL: bias_add_nchw
func.func @bias_add_nchw(%arg0: tensor<1x256x150x150xf32>, %arg1: tensor<256xf32>) -> tensor<1x256x150x150xf32> {
  // CHECK: (%[[ARG0:.*]]: tensor<1x256x150x150xf32>, %[[ARG1:.*]]: tensor<256xf32>)
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {

// CHECK-LABEL: conv_2d_bf16
func.func @conv_2d_bf16(%arg0 : tensor<256x32x32x3xbf16>, %arg1 : tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16> {

func.func private @sum_reducer(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func @xla_func_0(%arg0: tensor<7xf32>, %arg1: tensor<f32>) -> tensor<10xf32> {
  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>