}

// invokeMacro pushes onto the input Stack a Slice that holds the macro definition with the actual
// parameters substituted for the formals.
// Invoking a macro does not touch the PC/line history.
func (in *Input) invokeMacro(macro *Macro) {
	// If the macro has no arguments, just substitute the text.
	if macro.args == nil {
		in.Push(NewSlice(in.Base(), in.Line(), macro.tokens))
		return
	}
	tok := in.Stack.Next()

In the runtime package, assembly code can include <code>go_tls.h</code>, which defines
an OS- and architecture-dependent macro <code>get_tls</code> for accessing this register.
The <code>get_tls</code> macro takes one argument, which is the register to load the
<code>g</code> pointer into.
</p>

<p>
For example, the sequence to load <code>g</code> and <code>m</code>

        Map<String, Object> attributes = new HashMap<>();
        attributes.put("source-highlighter", "coderay");
        attributes.put("toc", "macro");
        attributes.put("toclevels", 2);

        // TODO: This breaks if version is changed later
        String versionUrl = DOCS_GRADLE_ORG + project.getVersion();

//	LAND/LXOR/LOR imm ',' rreg
//	{
//		outcode(int($1), &$2, 0, &$4);
//	}
	AND	$11, R17, R7	// 3227000b
	XOR	$341, R1, R23	// 38370155
	OR	$254, R25, R13	// 372d00fe
//
// move immediate: macro for lui+or, addi, addis, and other combinations
//
//	LMOVW imm ',' rreg
//	{
//		outcode(int($1), &$2, 0, &$4);
//	}
	MOVW	$1, R1
	MOVV	$1, R1

//	LMOVW ximm ',' rreg
//	{

pkg debug/macho, const FlagBindsToWeak uint32
pkg debug/macho, const FlagCanonical = 16384
pkg debug/macho, const FlagCanonical uint32
pkg debug/macho, const FlagDeadStrippableDylib = 4194304
pkg debug/macho, const FlagDeadStrippableDylib uint32
pkg debug/macho, const FlagDyldLink = 4
pkg debug/macho, const FlagDyldLink uint32
pkg debug/macho, const FlagForceFlat = 256
pkg debug/macho, const FlagForceFlat uint32

    visibility = ["//visibility:public"],
)

cc_library(
    name = "c_api_macros_hdrs",
    hdrs = [
        "c_api_macros.h",
    ],
    visibility = ["//visibility:public"],
)

cc_library(
    name = "c_api_macros",
    hdrs = [
        "c_api_macros.h",
        "c_api_macros_internal.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [

Ce découplage des parties, et le fait d'être un "micro-framework" qui puisse être étendu pour couvrir exactement ce
qui est nécessaire, était une caractéristique clé que je voulais conserver.

Compte tenu de la simplicité de Flask, il semblait bien adapté à la création d'API. La prochaine chose à trouver était un "Django REST Framework" pour Flask.

!!! check "A inspiré **FastAPI** à"

    deps = [
        ":immediate_execution_context",
        "//tensorflow/c:conversion_macros",
    ],
)

cc_library(
    name = "tfe_cancellation_manager_internal",
    hdrs = ["tfe_cancellation_manager_internal.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        "//tensorflow/c:conversion_macros",
        "//tensorflow/core:framework",
    ],
)

cc_library(

Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

!!! check "Inspired **FastAPI** to"
    Be a micro-framework. Making it easy to mix and match the tools and parts needed.

    Have a simple and easy to use routing system.


### <a href="https://requests.readthedocs.io" class="external-link" target="_blank">Requests</a>

#ifndef TENSORFLOW_C_C_API_EXPERIMENTAL_H_
#define TENSORFLOW_C_C_API_EXPERIMENTAL_H_

#include <stddef.h>
#include <stdint.h>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/eager/c_api.h"

// --------------------------------------------------------------------------
// Experimental C API for TensorFlow.
//
// The API here is subject to changes in the future.