"vendor.com/device=device1",
							"vendor.com/device=device2",
						},
						"test-plugin2": {
							"vendor.com/device=device1",
							"vendor.com/device=device2",
						},
					},
				},
			},
			expectedResult: []kubecontainer.CDIDevice{
				{
					Name: "vendor.com/device=device1",
				},
				{
					Name: "vendor.com/device=device1",
				},
				{

// configuring the persistor used in the DeviceCompiler. Please note that
// non-XLA devices aren't supported yet. This is because:
// 1. PjRtClient doesn't support data transfer for non-XLA devices yet
// 2. Fetching the PjRtClient for non-XLA devices is also not supported yet
Status GetOrCreatePjRtDeviceCompilerAndProfiler(
    const OpKernelContext& ctx, const XlaPlatformInfo& platform_info,

// such as TPUExecute or XlaExecute depending on the device type and specific
// host runtime. Also does some optimization. Will return an error if it fails.
// The output Runtime ops depends on both Device Type and Runtime Host.
//
// Input:
//     Tensorflow Dialect MLIR with tf_device.cluster ops and virtual devices.
//     xla_device_type - The device type that is being targeted.
// Output:

// RUN: tf-opt %s -split-input-file -tf-xla-rewrite | FileCheck %s


module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:GPU:0"]} {
  // CHECK-LABEL: func.func @convert_cluster_func
  func.func @convert_cluster_func(%arg0: tensor<i32>) -> tensor<i32> {
    // CHECK: "tf.XlaLaunch"(%arg0) <{function = @func, operandSegmentSizes = array<i32: 0, 1, 0>}> : (tensor<i32>) -> tensor<i32>

    device_list_for_alias.reserve(device_list.size());

    for (auto device : device_list)
      device_list_for_alias.emplace_back(
          mlir::cast<StringAttr>(device).getValue());

    devices.insert({device_alias, device_list_for_alias});
  }

  OpBuilder builder(replicate);
  builder.setInsertionPoint(while_op);
  // Create per-device variables for formatting state, and add them to the while
  // loop.

  func.func @main(%arg0: tensor<*xf32> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) {

}

Device* DeviceSetup::GetDevice(const string& device_name) {
  if (device_mgr_ == nullptr) {
    return nullptr;
  }

  string full_device_name = absl::StrCat(
      "/job:localhost/replica:0/task:0/device:", device_name, ":0");
  Device* device;
  TF_CHECK_OK(device_mgr_->LookupDevice(full_device_name, &device));
  return device;
}

    // The name of the compilation device (e.g., "XLA_CPU_JIT");
    string compilation_device_name;

    // If 'use_multiple_streams' is true, we create separate streams for
    // compute, host-to-device, and device-to-host communication.
    bool use_multiple_streams = false;

    // If true, the XLA devices with the same device ordinal will share the same
    // compute stream. Otherwise each XLA device will having their own compute

             %arg2: !tf_res_md_f32 {tf.device = "/device:TPU:0"},
             %arg3: !tf_res_md_f32 {tf.device = "/device:TPU:1"}) {

    %0 = "tf.Const"() {value = dense<100> : tensor<i32>} : () -> tensor<i32>
    // CHECK: %[[STATE0:.*]] = "tf.VarHandleOp"()
    // CHECK-SAME: device = "/device:TPU:0"
    // CHECK: %[[STATE1:.*]] = "tf.VarHandleOp"()
    // CHECK-SAME: device = "/device:TPU:1"

  tf_device.replicate() {n = 2 : i32, devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}} {
    tf_device.return
  }
  func.return

// CHECK:      tf_device.replicate
// CHECK-SAME: devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}
// CHECK-SAME: n = 2
// CHECK-NEXT:   tf_device.return