CHECK:  %[[LSTM:[a-z0-9]*]] = "tfl.unidirectional_sequence_lstm"(%arg2, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg0, %arg1, %[[CST_0]], %[[CST_1]], %arg0, %arg0, %arg0, %arg0) <{fused_activation_function = "NONE", time_major = true}> : (tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>,...

func.func @test_batch_matmul_to_einsum(%arg0: tensor<1x2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x2x4xf32> {
  // CHECK-LABEL: test_batch_matmul_to_einsum
  // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>

//go:build asan

#include "go_asm.h"
#include "textflag.h"

#define RARG0 R4
#define RARG1 R5
#define RARG2 R6
#define RARG3 R7
#define FARG  R8

// Called from instrumented code.
// func runtime·doasanread(addr unsafe.Pointer, sz, sp, pc uintptr)
TEXT	runtime·doasanread(SB), NOSPLIT, $0-32
	MOVV	addr+0(FP), RARG0
	MOVV	sz+8(FP), RARG1
	MOVV	sp+16(FP), RARG2
	MOVV	pc+24(FP), RARG3

    %2 = "tf.MyStringMatch"(%arg0, %arg1) : (tensor<!tf_type.string>, tensor<!tf_type.string>) -> tensor<!tf_type.string>
    // CHECK: %[[PREFIX:.*]] = "tf.IsStringPrefix"
    %3 = "tf.IsStringPrefix"(%arg0, %arg1) : (tensor<!tf_type.string>, tensor<!tf_type.string>) -> tensor<!tf_type.string>
    // CHECK: tf_device.return %[[MATCH]], %[[PREFIX]]

}

func.func @while_body(%arg0: tensor<*xi32>) -> (tensor<*xi32>) {
  %0 = "tf.Rank"(%arg0) : (tensor<*xi32>) -> tensor<i32>
  %1 = "tf.Add"(%0, %arg0): (tensor<i32>, tensor<*xi32>) -> tensor<*xi32>
  func.return %1: tensor<*xi32>
}

func.func @while_cond(%arg0: tensor<*xi32>) -> tensor<*xi1> {
  %cst = arith.constant dense<10> : tensor<i32>
  %0 = "tf.Less"(%arg0, %cst) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi1>

  func.return %0#0 : tensor<i32>
}

func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}
func.func @while_cond_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i1> {

// Identity function to make the exporter happy
func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> {
  func.return %arg0 : tensor<4xi8>
}

//CHECK:func.func @main(%arg0: tensor<4xi8>) -> tensor<4xi8> attributes {tf.entry_function = {inputs = "arg0", outputs = "arg0"}} {
//CHECK: return %arg0 : tensor<4xi8>
//CHECK:}

func.func @custom_tf_op(%arg0: tensor<1x320x1x1xf32>, %arg1: tensor<2xi32>) -> tensor<1x1600x1x1xf32> {

// CHECK-LABEL: module
module {
  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-NOT:    function_list
    // CHECK-SAME:   module = ""

// RUN: odml-to-stablehlo-opt %s -tfl-parse-stablehlo-ops | FileCheck %s

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.subtract", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

    return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]