* #toMap(Iterable, Function)}.
   *
   * <p>Specifically, for each {@code k} in the backing set, the returned map has an entry mapping
   * {@code k} to {@code function.apply(k)}. The {@code keySet}, {@code values}, and {@code
   * entrySet} views of the returned map iterate in the same order as the backing set.
   *
   * <p>Modifications to the backing set are read through to the returned map. The returned map

   * #toMap(Iterable, Function)}.
   *
   * <p>Specifically, for each {@code k} in the backing set, the returned map has an entry mapping
   * {@code k} to {@code function.apply(k)}. The {@code keySet}, {@code values}, and {@code
   * entrySet} views of the returned map iterate in the same order as the backing set.
   *
   * <p>Modifications to the backing set are read through to the returned map. The returned map

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 233
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",

[build-system]
requires = ["pdm-backend"]
build-backend = "pdm.backend"

[project]
name = "fastapi"
dynamic = ["version"]
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE"]
requires-python = ">=3.10"
authors = [
    { name = "Sebastián Ramírez", email = "******@****.***" },
]
classifiers = [

import java.util.concurrent.BlockingQueue;
import java.util.concurrent.TimeUnit;
import org.jspecify.annotations.NullUnmarked;
import org.jspecify.annotations.Nullable;

/**
 * A bounded {@linkplain BlockingQueue blocking queue} backed by an array. This queue orders
 * elements FIFO (first-in-first-out). The head of the queue is that element that has been

        `tf.data.experimental.enable_debug_mode()`.
*   `tf.lite`
    *   Enabled the new MLIR-based quantization backend by default
        *   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)

     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to
     * ensure that completed write operations performed by other threads are noticed. For most
     * purposes, the "count" field, tracking the number of elements, serves as that volatile

 *
 * <ul>
 *   <li>for an unnested {@code lock()} and {@code unlock()}, a cycle detecting lock takes 38ns as
 *       opposed to the 24ns taken by a plain lock.
 *   <li>for nested locking, the cost increases with the depth of the nesting:
 *       <ul>
 *         <li>2 levels: average of 64ns per lock()/unlock()
 *         <li>3 levels: average of 77ns per lock()/unlock()

common:mkl --define=build_with_mkl=true --define=enable_mkl=true
common:mkl --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl --define=build_with_openmp=true
common:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

indexer.thread.dump.enabled=true
# Maximum number of unprocessed documents for the indexer.
indexer.unprocessed.document.size=1000
# Whether to enable click count tracking in the indexer.
indexer.click.count.enabled=true
# Whether to enable favorite count tracking in the indexer.
indexer.favorite.count.enabled=true
# Commit margin time (ms) for webfs in the indexer.
indexer.webfs.commit.margin.time=5000