Pour appeler une fonction définie avec `async def`, vous devez utiliser `await`. Donc ceci ne marche pas :

```Python
# Ceci ne fonctionne pas, car get_burgers a été défini avec async def
burgers = get_burgers(2)
```

---

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

did not appear, and after a minute or two she walked on in the
direction in which the March Hare was said to live.  `I've seen
hatters before,' she said to herself; `the March Hare will be
much the most interesting, and perhaps as this is May it won't be
raving mad--at least not so mad as it was in March.'  As she said
this, she looked up, and there was the Cat again, sitting on a
branch of a tree.

pkg debug/elf, const R_LARCH_ADD32 R_LARCH #46229
pkg debug/elf, const R_LARCH_ADD64 = 51 #46229
pkg debug/elf, const R_LARCH_ADD64 R_LARCH #46229
pkg debug/elf, const R_LARCH_ADD8 = 47 #46229
pkg debug/elf, const R_LARCH_ADD8 R_LARCH #46229
pkg debug/elf, const R_LARCH_COPY = 4 #46229
pkg debug/elf, const R_LARCH_COPY R_LARCH #46229
pkg debug/elf, const R_LARCH_IRELATIVE = 12 #46229
pkg debug/elf, const R_LARCH_IRELATIVE R_LARCH #46229

"""
    fileSystem.createDirectory(cache.directoryPath)
    writeFile(cache.directoryPath, "$urlKey.0", entryMetadata)
    writeFile(cache.directoryPath, "$urlKey.1", entryBody)
    writeFile(cache.directoryPath, "journal", journalBody)
    cache = Cache(fileSystem, cache.directory.path.toPath(), Int.MAX_VALUE.toLong())
    client =
      client.newBuilder()
        .cache(cache)
        .build()

# Combine TF public build cache and local disk cache
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache --disk_cache=$TFCI_OUTPUT_DIR/cache"
else
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache --disk_cache=$TFCI_OUTPUT_DIR/cache"

            }
        }
    }
}


private class BuilderCache<From, To : KtSymbol> {
    private val cache = ContainerUtil.createConcurrentWeakKeySoftValueMap<From, To>()

    inline fun <reified S : To> cache(key: From, calculation: () -> S): S {
        val value = cache.getOrPut(key, calculation)
        return value as? S
            ?: errorWithAttachment("Cannot cast ${value::class} to ${S::class}") {

 * the cache; the cache may delete or overwrite files from its directory. It is an error for
 * multiple processes to use the same cache directory at the same time.
 *
 * This cache limits the number of bytes that it will store on the filesystem. When the number of
 * stored bytes exceeds the limit, the cache will remove entries in the background until the limit
 * is satisfied. The limit is not strict: the cache may temporarily exceed it while waiting for

.idea
.ipynb_checkpoints
.mypy_cache
.vscode
__pycache__
.pytest_cache
htmlcov
dist
site
.coverage
coverage.xml
.netlify
test.db
log.txt
Pipfile.lock
env3.*
env
docs_build
site_build
venv
docs.zip
archive.zip

# vim temporary files
*~
.*.sw?
.cache

# macOS

TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

# 3. Push results to public build cache (unique cache for MacOS)
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push"
else
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache_push"