auto rewrite = [&](auto op_type) {
    auto empty = rewriter.getStringAttr("");
    ReplaceTfOpWithNewOp<typename decltype(op_type)::CallOp>(
        rewriter, op, op.getResultTypes(), op.getInput(), func,
        /*config=*/empty, /*config_proto=*/empty, /*executor_type=*/empty);
  };

  if (op.getIsStateless())
    rewrite(CallOpType<PartitionedCallOp>{});
  else

	"cmd/compile/internal/typecheck"
	"cmd/compile/internal/types"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Rewrite tree to use separate statements to enforce
// order of evaluation. Makes walk easier, because it
// can (after this runs) reorder at will within an expression.
//
// Rewrite m[k] op= r into m[k] = m[k] op r if op is / or %.
//
// Introduce temporaries as needed by runtime routines.

  let dependentDialects = ["tf_device::TensorFlowDeviceDialect"];
}

def XlaRewritePass : Pass<"tf-xla-rewrite", "mlir::ModuleOp"> {
  let summary = "Rewrites partition calls into Xla launch ops to make the attached function run on XLA.";

  let description = [{
    This pass rewrites `tf.PartitionedCall` and `tf.StatefulPartitionedCall`
    operations with `_xla_compile_device_type` attribute in a

  // runs, embedding attributes are stripped and the sequencing pass will have
  // no effect. If the pipelining pass doesn't run, embedding attributes are
  // preserved and the sequencing rewrite will trigger.
  pm.addPass(mlir::TFDevice::CreateEmbeddingPipeliningPass());
  pm.addPass(mlir::TFDevice::CreateEmbeddingSequencingPass());
  pm.addPass(tensorflow::tf2xla::internal::CreateTPUClusterFormationPass(

		}
	}

	out := string(cmdOut)

	if !strings.HasSuffix(out, "\n") {
		out = out + "\n"
	}

	// Replace workDir with $WORK
	out = replacePrefix(out, sh.workDir, "$WORK")

	// Rewrite mentions of dir with a relative path to dir
	// when the relative path is shorter.
	for {
		// Note that dir starts out long, something like
		// /foo/bar/baz/root/a
		// The target string to be reduced is something like

// Compiles a TensorFlow Graph (already converted to MLIR, imported with
// tf_executor dialect still present) into XLA HLO, generates all accompanying
// metadata and stores them in CompilationResult. This will rewrite arguments
// and run the TensorFlow standard pipeline prior to invoking
// `CompileMlirToXlaHlo`.
ABSL_DEPRECATED("Use v2/legalize_tf.h::LegalizeMlirToHlo instead.")
Status CompileGraphToXlaHlo(

func (v *Value) resetArgs() {
	for _, a := range v.Args {
		a.Uses--
	}
	v.argstorage[0] = nil
	v.argstorage[1] = nil
	v.argstorage[2] = nil
	v.Args = v.argstorage[:0]
}

// reset is called from most rewrite rules.
// Allowing it to be inlined increases the size
// of cmd/compile by almost 10%, and slows it down.
//
//go:noinline
func (v *Value) reset(op Op) {
	if v.InCache {
		v.Block.Func.unCache(v)
	}

      text: use a function call in (Eventually|Consistently)

    # https://github.com/kubernetes/kubernetes/issues/117288#issuecomment-1507012435
    - linters:
        - gocritic
      text: "ifElseChain: rewrite if-else to switch statement"

    # Only packages listed here opt into the strict "exported symbols must be documented".
    #

// Using the resource to operations map, it adds (1) a control dependency
// from chain_src to all the operations that read/write to a resource of the
// equivalence class, and (2) a control dependency from all the operations that
// read/write to a resource of the class to the chain_sink operation.
void ChainResourceOps(
    func::FuncOp func, ResourceToOpsMapTy& chain_resource_to_ops_map,

			// TODO: if both start and end are constants we should rewrite such that the comparison
			// is against zero and nxt is ++ or -- operation
			// That means:
			//	for i := 2; i < 11; i += 2 {
			// should be rewritten to:
			//	for i := 5; 0 < i; i-- {
			continue
		}

		if end.Block == ind.Block {
			// we can't rewrite loops where the condition depends on the loop body