List<Integer> fromList = Lists.newLinkedList(SOME_SEQUENTIAL_LIST);
    List<String> list = transform(fromList, SOME_FUNCTION);
    assertTransformIterator(list);
  }

  /**
   * This test depends on the fact that {@code AbstractSequentialList.iterator} transforms the
   * {@code iterator()} call into a call on {@code listIterator(int)}. This is fine because the
   * behavior is clearly documented so it's not expected to change.

    List<Integer> fromList = Lists.newLinkedList(SOME_SEQUENTIAL_LIST);
    List<String> list = transform(fromList, SOME_FUNCTION);
    assertTransformIterator(list);
  }

  /**
   * This test depends on the fact that {@code AbstractSequentialList.iterator} transforms the
   * {@code iterator()} call into a call on {@code listIterator(int)}. This is fine because the
   * behavior is clearly documented so it's not expected to change.

```

⤴️ 👥 🔜 📣 *➡ 🛠️ 🔢* & 🔗 🍵 `async def`, ⏮️ 😐 `def`,:

```Python hl_lines="2"
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
    db_user = crud.get_user(db, user_id=user_id)
    ...
```

/// info

🚥 👆 💪 🔗 👆 🔗 💽 🔁, 👀 [🔁 🗄 (🔗) 💽](../advanced/async-sql-databases.md){.internal-link target=_blank}.

///

			// As per "Permission" section in
			// https://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectGET.html
			// If the object you request does not exist,
			// the error Amazon S3 returns depends on
			// whether you also have the s3:ListBucket
			// permission.
			// * If you have the s3:ListBucket permission
			//   on the bucket, Amazon S3 will return an
			//   HTTP status code 404 ("no such key")

# instead depend on the dummy plugins in @local_tsl//tsl/platform/default/build_config
# and use header only targets.
# TODO(ddunleavy): This seems completely broken. :tensorflow_cc depends on
# cuda_platform from tf_additional_binary_deps and this doesn't break.
check_deps(
    name = "cuda_plugins_check_deps",
    disallowed_deps = if_static(
        [],
        otherwise = [

such a program is covered only if its contents constitute a work based
on the Library (independent of the use of the Library in a tool for
writing it).  Whether that is true depends on what the Library does
and what the program that uses the Library does.

  1. You may copy and distribute verbatim copies of the Library's
complete source code as you receive it, in any medium, provided that

 *
 * <p>When calling {@link ScaleAndIndex#compute} (in {@linkplain ScaleAndIndexes#compute either
 * form}), the memory requirement is 8*N bytes for the copy of the dataset plus an overhead which is
 * independent of N (but depends on the quantiles being computed). When calling {@link
 * ScaleAndIndex#computeInPlace computeInPlace} (in {@linkplain ScaleAndIndexes#computeInPlace

  optional string driver = 1;

  // fsType is the Filesystem type to mount.
  // Must be a filesystem type supported by the host operating system.
  // Ex. "ext4", "xfs", "ntfs". The default filesystem depends on FlexVolume script.
  // +optional
  optional string fsType = 2;

  // secretRef is Optional: SecretRef is reference to the secret object containing

		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}
	jpool.ResizeWorkers(workers)

	randomWait := func() time.Duration {
		// randomWait depends on the number of nodes to avoid triggering resume and cleanups at the same time.
		return time.Duration(rand.Float64() * float64(time.Duration(globalEndpoints.NEndpoints())*time.Hour))
	}

	go func() {

build:rbe_cross_compile_base --config=rbe_base
build:rbe_cross_compile_base --remote_instance_name=projects/tensorflow-testing/instances/default_instance

# XLA depends on some local Python headers that are configured as Genrule. They
# are present on the local host machine but not on the remote execution machine,
# leading to build failures. To resolve the issue, the following line is added