#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project

namespace mlir {
namespace TFDevice {

// For architectures that support accelerated embedding lookups, this pass will
// rewrite the graph to use pipelining for better device utilization.
std::unique_ptr<OperationPass<ModuleOp>> CreateEmbeddingSequencingPass();

#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Helper class for managing data transfers between host and accelerator
// devices using PjRt.
class PjRtDeviceContext : public DeviceContext {
 public:
  explicit PjRtDeviceContext(
      XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,

  let summary = "Rewrite graph for embedding pipelining";
  let constructor = "TFDevice::CreateEmbeddingPipeliningPass()";
    let description = [{
    For architectures that support accelerated embedding lookups, this pass will
    rewrite the graph to use pipelining for better device utilization.
  }];
}

def EmbeddingSequencingPass : Pass<"tf-embedding-sequencing", "mlir::ModuleOp"> {

#include "tensorflow/core/common_runtime/local_device.h"
#include "tensorflow/core/framework/device_base.h"

namespace tensorflow {

// tensorflow::PjRtBaseDevice replaces the deprecated tensorflow::XlaDevice.
// This accelerator agnostic device is mainly used to store metadata.
class PjRtBaseDevice : public LocalDevice {
 public:
  // Stores metadata about the PjRtBaseDevice.
  class Metadata {
   public:

        uses: actions/setup-java@v4
        with:
          distribution: 'zulu'
          java-version: 17

      - name: Enable KVM group perms
        # https://github.blog/changelog/2023-02-23-hardware-accelerated-android-virtualization-on-actions-windows-and-linux-larger-hosted-runners/
        run: |
          echo 'KERNEL=="kvm", GROUP="kvm", MODE="0666", OPTIONS+="static_node=kvm"' | sudo tee /etc/udev/rules.d/99-kvm4all.rules

# is a request for 2 SCSI formatted and mounted SSDs and 1 NVMe block device SSD.
NODE_LOCAL_SSDS_EXT=${NODE_LOCAL_SSDS_EXT:-}
# Accelerators to be attached to each node. Format "type=<accelerator-type>,count=<accelerator-count>"
# More information on available GPUs here - https://cloud.google.com/compute/docs/gpus/
NODE_ACCELERATORS=${NODE_ACCELERATORS:-""}
export REGISTER_MASTER_KUBELET=${REGISTER_MASTER:-true}

- Introduced a new alpha feature gate, `SELinuxMount`, which can now be enabled to accelerate SELinux relabeling. ([#123157](https://github.com/kubernetes/kubernetes/pull/123157), [@jsafrane](https://github.com/jsafrane))

    std::function<bool(Operation*)> is_ignored_op) {
  // Iteratively find clusters of different targets within the `block`.
  // Whenever we see an operation that is assigned to an accelerator target
  // (ie. get_target(op) != ""), we try to merge it into the last cluster
  // of same target. If that is infeasible (say because of violating
  // def-before-use), create a new cluster with that operation and move on.

  let summary = "Lifting resource operations out of device computation";
  let description = [{
    This pass lifts resource variable operations outside of device computation.
    This is useful because a lot of accelerator devices can not interact with
    resource variables directly..

    Here is a simple example in TensorFlow where a device doubles the value of a
    TensorFlow resource variable and returns new value:

    ```mlir

implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments