if err != nil {
		if _, err := os.Stat(device); os.IsNotExist(err) {
			return nil
		}
		klog.V(2).Infof("Failed to remove loopback device: %s: %v %s", device, err, out)
		return fmt.Errorf("losetup -d %s failed: %v", device, err)
	}
	return nil
}

// getLoopDeviceFromSysfs finds the backing file for a loop
// device from sysfs via "/sys/block/loop*/loop/backing_file".

      // CHECK: %[[LAUNCH_OUTPUT:[0-9]*]] = "tf_device.launch_func"(%[[A_OUTPUT]]) <{device = "/device:test_device:0", func = @[[LAUNCH:.*]]}>
      %3 = "tf_device.launch"() ({
        %4 = "tf.B"(%2) : (tensor<?xi32>) -> tensor<?xi32>
        tf_device.return %4 : tensor<?xi32>
      }) {device = "/device:test_device:0"} : () -> tensor<?xi32>

      // CHECK: tf_executor.yield %[[LAUNCH_OUTPUT]]
      tf_executor.yield %3 : tensor<?xi32>
    }

#define DEBUG_TYPE "tf-resource-device-inference"

namespace mlir {
namespace TF {

namespace {
constexpr char kDeviceAttr[] = "device";
constexpr char kFuncDeviceAttr[] = "tf.device";

#define GEN_PASS_DEF_RESOURCEDEVICEINFERENCEPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

// A pass that propagates device assignment of resources on a module. It

//     OnTFDeviceView(params->device->struct_size);
//     params->device = { SP_DEVICE_STRUCT_SIZE };
//     params->device->device_handle = get_my_device_handle(device->ordinal);
//     params->device->ordinal = params->ordinal;
//     ...
//   }
//
//   void destroy_device(const SP_Platform* platform, SP_Device* device) {
//     delete_my_device_handle(device->device_handle);
//   }
//
//   void SE_InitPlugin(

	if vendor == "" {
		return "", "", device, fmt.Errorf("unqualified device %q, missing vendor", device)
	}
	if class == "" {
		return "", "", device, fmt.Errorf("unqualified device %q, missing class", device)
	}
	if name == "" {
		return "", "", device, fmt.Errorf("unqualified device %q, missing device name", device)
	}

	if err := validateVendorName(vendor); err != nil {

namespace mlir {
namespace TF {

namespace {

const char kDeviceAttr[] = "device";
const char kDeviceGpu[] = "GPU";

std::optional<std::string> GetOpDevice(mlir::Operation *op) {
  mlir::StringAttr device = op->getAttrOfType<mlir::StringAttr>(kDeviceAttr);
  if (!device || device.getValue().empty()) {
    return std::nullopt;
  }
  tensorflow::DeviceNameUtils::ParsedName parsed_name;

        "Cannot get device metadata from non-PJRT device \"", device->name(),
        "\". GetMetadata must only be called on a device derived from "
        "PjRtBaseDevice. Either an internal bug has been triggered, or an "
        "XLA-specific op has been placed on the wrong device.");
  }
  return &pjrt_device->metadata_;
}

    // expected-remark@+1 {{Cost: 2}}
    %29 = "tf.Cast"(%dense_values#1) {Truncate = false, device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<?xi64>) -> tensor<?xf32>
    // expected-remark@+1 {{Cost: 2}}

node {
  name: "G4SM2HGMB5"
  op: "VarHandleOp"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@G4SM2HGMB5/Read/ReadVariableOp"
      }
    }
  }
  attr {
    key: "container"
    value {
      s: ""
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {

    // CHECK-SAME: <{device = "tpu0"}>
    // CHECK: %[[B_OUTPUT:[0-9]*]] = "tf.B"(%[[A_OUTPUT]]) : (tensor<?xi32>) -> tensor<?xi32>
    %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>

    // CHECK: %[[C_OUTPUT:[0-9]*]] = "tf.C"(%[[A_OUTPUT]], %[[B_OUTPUT]]) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>