and library of compiler utilities that sits between the model representation
and low-level compilers/executors that generate hardware-specific code.

MLIR is, at its heart, a flexible infrastructure for modern optimizing
compilers. This means it consists of a specification for intermediate
representations (IR) and a code toolkit to perform transformations on that
representation. (In compiler parlance, as you move from higher-level

	default:
		panic("nonblock")
	{{end}}
	{{/*  Dummy send, receive to keep compiler from optimizing select. */}}
	{{if .Maybe}}
	case dummy <- 1:
		panic("dummy send")
	{{end}}
	{{if .Maybe}}
	case <-dummy:
		panic("dummy receive")
	{{end}}
	{{/*  Nil channel send, receive to keep compiler from optimizing select. */}}
	{{if .Maybe}}
	case nilch <- 1:
		panic("nilch send")
	{{end}}
	{{if .Maybe}}

        when:
        inFile.copyTo(copy)
        inFile.delete()
        inFile.createLink(copy)
        run("work")

        /*
         * This documents the current behavior, which is optimizing
         * for performance at the expense of not detecting some corner
         * cases. If there actually is a task that needs to distinguish
         * between links and real files, we should probably provide an

  pm.addNestedPass<func::FuncOp>(createNchwConvolutionToNhwcPass());

  // Folds `stablehlo.constant`->`stablehlo.transpose` patterns, which is often
  // generated as by-products after optimizing dimension numbers (e.g.
  // NCHW->NHWC convolution conversion).
  pm.addNestedPass<func::FuncOp>(createFoldConstantTransposePass());
  pm.addPass(CreateLiftQuantizableSpotsAsFunctionsPass(quantization_specs));

import (
	"net/http"
)

// WithCacheControl sets the Cache-Control header to "no-cache, private" because all servers are protected by authn/authz.
// see https://developers.google.com/web/fundamentals/performance/optimizing-content-efficiency/http-caching#defining_optimal_cache-control_policy
func WithCacheControl(handler http.Handler) http.Handler {
	return http.HandlerFunc(func(w http.ResponseWriter, req *http.Request) {

<<dependency_management_terminology.adoc#dependency_management_terminology,Working with Dependencies>> :: Add dependencies to your build.
<<performance.adoc#performance_gradle,Optimizing Builds>> :: Use caches to optimize your build and understand the Gradle daemon, incremental builds and file system watching.

namespace TF {

inline constexpr char kStandardPipelineBefore[] = "standard_pipeline_before";
inline constexpr char kStandardPipelineAfter[] = "standard_pipeline_after";

// Runs all passes involved in transforming or optimizing an MLIR graph without
// any target specialization. When enable_logging is true, enables
// tensorflow::BridgeLogger. When enable_inliner is true, enables the inliner
// pass.
ABSL_DEPRECATED(

	it also runs a bunch of user-supplied malloc hooks.

	So we choose strncpy(_, _, 0): it requires an extra header,
	but it's standard and should be very efficient.

	GCC 7 has an unfortunate habit of optimizing out strncpy calls (see
	https://golang.org/issue/21196), so the arguments here need to be global
	variables with external linkage in order to ensure that the call traps all the
	way down into libc.
	*/

import org.gradle.api.internal.artifacts.ivyservice.resolveengine.excludes.specs.ExcludeSpec;

import java.util.Iterator;
import java.util.Set;

/**
 * This factory is responsible for optimizing in special cases: null parameters,
 * list with 2 elements, ... and should be at the top of the delegation chain.
 */
public class OptimizingExcludeFactory extends DelegatingExcludeFactory {

image::build-scan-1.png[]

The information that scans collect can be an invaluable resource when troubleshooting, collaborating on, or optimizing the performance of your builds.