#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassOptions.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"

namespace mlir {

// Creates a pass that breaks up an island with multiple ops into multiple

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

include "mlir/Pass/PassBase.td"

// TF dialect passes.

def TensorflowGPUFusion : Pass<"tf-gpu-op-fusion", "mlir::func::FuncOp"> {
  let summary = "Fusion optimization for GPU targets";
  let description = [{
    This pass is performing fusion specific to GPU targets. This is an ad-hoc
    pass for now, but should be integrated with some notion of "target" in the

  // Skip function graphs as MlirOptimizationPassRegistry_ will be used instead.
  // Skip if no underlying pass was registered.
  if (options.is_function_graph || !registry_->pass()) return absl::OkStatus();

  auto pass = registry_->pass();
  auto pass_state =
      pass->GetPassState(options.device_set, options.session_options->config,
                         **options.graph, *options.flib_def);

#include <memory>
#include <string>
#include <vector>

#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/Passes.h"  // from @llvm-project

  options.graph = &graph;
  options.flib_def = &flib_def;
  options.session_options = &sess_options;

  for (auto pass : passes_) {
    assert(pass != nullptr);
    Status status = pass->Run(options);
    if (!status.ok()) {
      mlir::emitError(mlir::UnknownLoc::get(&ctx))
          << pass->name() << ": " << status.message();
      return signalPassFailure();
    }
  }

  // Convert Graph to MLIR

@SuppressWarnings("all")
public class Execution implements java.io.Serializable {

    // --------------------------/
    // - Class/Member Variables -/
    // --------------------------/

    /**
     * Configuration to pass to the goals.
     */
    private Object configuration;

    /**
     * Field goals.
     */
    private java.util.List<String> goals;

    // -----------/
    // - Methods -/
    // -----------/

	return authenticateSSHConnection(c, key, nil)
}

func sshPasswordAuth(c ssh.ConnMetadata, pass []byte) (*ssh.Permissions, error) {
	return authenticateSSHConnection(c, nil, pass)
}

func authenticateSSHConnection(c ssh.ConnMetadata, key ssh.PublicKey, pass []byte) (*ssh.Permissions, error) {
	user, found := strings.CutSuffix(c.User(), "=ldap")
	if found {

     */
    private String id;

    /**
     * Field executions.
     */
    private java.util.List<Execution> executions;

    /**
     * Configuration to pass to all goals run in this phase.
     */
    private Object configuration;

    // -----------/
    // - Methods -/
    // -----------/

    /**
     * Method addExecution.
     *

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/Location.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"

    compatible_with = get_compatible_with_portable(),
    tbl_outs = [
        (
            [
                "-gen-pass-decls",
                "-name=TensorFlow",
            ],
            "tf_passes.h.inc",
        ),
        (
            ["-gen-pass-doc"],
            "g3doc/_includes/tf_passes.md",
        ),
    ],
    tblgen = "@llvm-project//mlir:mlir-tblgen",