#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v1 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called via the TF1 Session
// API.
// These transformations take as input a Tensorflow Graph as an MLIR Module
// and transforms the module in place to cluster the given ops for compilation

	testCases := []struct {
		description         string
		deviceIDs           []string
		expecteError        error
		expectedAnnotations []container.Annotation
	}{
		{
			description: "no devices",
			deviceIDs:   []string{},
		},
		{
			description:         "one device",
			deviceIDs:           []string{"vendor.com/class=device1"},

}

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import java.util.concurrent.locks.LockSupport;
import javax.annotation.CheckForNull;

/**
 * Works around an android bug, where parking for more than INT_MAX seconds can produce an abort
 * signal on 32 bit devices running Android Q.
 */
@J2ktIncompatible
@ElementTypesAreNonnullByDefault
final class OverflowAvoidingLockSupport {
  // Represents the max nanoseconds representable on a linux timespec with a 32 bit tv_sec

import java.util.concurrent.locks.LockSupport;
import javax.annotation.CheckForNull;

/**
 * Works around an android bug, where parking for more than INT_MAX seconds can produce an abort
 * signal on 32 bit devices running Android Q.
 */
@J2ktIncompatible
@ElementTypesAreNonnullByDefault
final class OverflowAvoidingLockSupport {
  // Represents the max nanoseconds representable on a linux timespec with a 32 bit tv_sec

  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  const int num_devices = TF_DeviceListCount(devices);

  const char* kCPUDevice = "CPU:0";
  for (int i = 0; i < num_devices; ++i) {
    const string name(TF_DeviceListName(devices, i, status.get()));
    if (TF_GetCode(status.get()) != TF_OK) {

// compilation. And there should be no host-device communication.
// CHECK: tf._TPUCompileMlir
// CHECK-NOT: tf.Reshape
// CHECK-NOT: tf._XlaHostComputeMlir

	rc.mountOrAttachVolumes()

	// Unmount volumes only when DSW and ASW are fully populated to prevent unmounting a volume
	// that is still needed, but it did not reach DSW yet.
	if readyToUnmount {
		// Ensure devices that should be detached/unmounted are detached/unmounted.
		rc.unmountDetachDevices()

		// Clean up any orphan volumes that failed reconstruction.
		rc.cleanOrphanVolumes()
	}

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

    "func.return"(%2) : (tensor<*xi1>) -> ()
  }) {sym_name = "main_while_cond_4225140", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], function_type = (tensor<i32>) -> tensor<*xi1>} : () -> ()