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:

    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.

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