==============================================================================*/

include "mlir/Pass/PassBase.td"

def OutlineCompositesPass: Pass<"outline-composites", "func::FuncOp"> {
  let summary = "Outlines specific patterns into composites.";
  let description = [{
    Outline specific patterns into composites. Specific patterns can be any
    sub-DAG within a single `Block*`. The signature of the new composite

  builder->create<func::ReturnOp>(cluster_return_op.getLoc(),
                                  cluster_return_op.getOperands());
  cluster_return_op.erase();
}

// Builds a function that outlines region attached to cluster_op or launch_op,
// and inserts built function into given module.
template <typename ClusterOrLaunchOp>
func::FuncOp BuildFunction(llvm::ArrayRef<Value> live_ins, ClusterOrLaunchOp op,

using mlir::SymbolUserOpInterface;
using mlir::func::FuncOp;

#define GEN_PASS_DEF_XLACLUSTERFORMATIONPASS
#include "tensorflow/compiler/mlir/tf2xla/internal/passes/clustering_passes.h.inc"

// Outlines partitioned call ops with `_XlaMustCompile` to device clusters.
struct XlaClusterFormationPass
    : public impl::XlaClusterFormationPassBase<XlaClusterFormationPass> {
  void runOnOperation() override;
};

import org.gradle.launcher.daemon.server.api.HandleStop
import org.gradle.test.precondition.Requires
import org.gradle.test.preconditions.IntegTestPreconditions
import org.gradle.test.preconditions.UnitTestPreconditions

/**
 * Outlines the lifecycle of the daemon given different sequences of events.
 *
 * These tests are a little different due to their async nature. We use the classes

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(QuantizeVariablesPass)
  explicit QuantizeVariablesPass() = default;

  void runOnOperation() override;

 private:
  // Outlines the regions of the WhileOp's cond and body and insert function
  // calls instead.
  void QuantizeVariable(OpBuilder &builder,
                        const std::vector<VarHandleOp> &var_handle_op);
};

# Introduction

This document outlines how to make hotfix binaries and containers for MinIO?. The main focus in this article is about how to backport patches to a specific branch and finally building binaries/containers.

## Pre-pre requisite

- A working knowledge of MinIO codebase and its various components.
- A working knowledge of AWS S3 API behaviors and corner cases.

## Pre-requisite for backporting any fixes

  rewriter.create<func::ReturnOp>(output_mul_op.getLoc(),
                                  output_mul_op.getResult());
  rewriter.clearInsertionPoint();
  return new_func;
}

// Outlines non-approximate GELU into a stablehlo composite.
//
//    -> mul 1/sqrt(2) -> erf -> add 1 ->
// in                                    mul
//    ---------> mul 0.5 --------------->
//

some form of isolation when dealing with untrusted data. As a result, this
document also outlines what issues we consider as TensorFlow security
vulnerabilities.

We recognize issues as vulnerabilities only when they occur in scenarios that we
outline as safe; issues that have a security impact only when TensorFlow is used

    SmallVector<Value, 16> yield_operands(call_op.getResults());
    builder.create<YieldOp>(island_op.getLoc(), yield_operands);
  }

  // Outline all the transitively called functions by moving them in the
  // outlined module.
  for (func::FuncOp func : outlined_module.getOps<func::FuncOp>()) {
    func.walk([&](Operation *op) {
      for (NamedAttribute attr : op->getAttrs()) {

// Package sync provides basic synchronization primitives such as mutual
// exclusion locks. Other than the [Once] and [WaitGroup] types, most are intended
// for use by low-level library routines. Higher-level synchronization is
// better done via channels and communication.
//
// Values containing the types defined in this package should not be copied.
package sync

import (
	"internal/race"
	"sync/atomic"