{"FFFFFFFFFFFFFFFF", 1<<64 - 1, ""},
		{"000000000000bogus", 0, "invalid byte in chunk length"},
		{"00000000000000000", 0, "http chunk length too large"}, // could accept if we wanted
		{"10000000000000000", 0, "http chunk length too large"},
		{"00000000000000001", 0, "http chunk length too large"}, // could accept if we wanted
	}
	for i := uint64(0); i <= 1234; i++ {
		tests = append(tests, testCase{in: fmt.Sprintf("%x", i), want: i})
	}

 * cases the consuming thread must call [close] when it has finished reading the response
 * body.
 *
 * ### The response body can be consumed only once.
 *
 * This class may be used to stream very large responses. For example, it is possible to use this
 * class to read a response that is larger than the entire memory allocated to the current process.

          .build(),
      )
    call1.execute().use { response -> response.body.string() }

    listener.shouldClose = true
    // Large headers are a likely reason the servers would cut off the connection before it completes sending
    // request headers.
    // 431 "Request Header Fields Too Large"
    val largeHeaders =
      Headers
        .Builder()
        .apply {
          repeat(32) {

    private final boolean largeFile;
    private int pid;
    private long byteOffset;
    private long lengthInBytes;

    /**
     * Constructs a LockingAndXRange with large file support.
     *
     * @param largeFile whether to use large file support (64-bit offsets)
     */
    public LockingAndXRange(final boolean largeFile) {
        this.largeFile = largeFile;
    }

    /**

from fastapi import FastAPI
from fastapi.responses import FileResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
async def main():

from docs_src.custom_response import tutorial009b_py39
from docs_src.custom_response.tutorial009b_py39 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial009b_py39.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

	opReadRune4 readOp = 4  // Read rune of size 4.
)

// ErrTooLarge is passed to panic if memory cannot be allocated to store data in a buffer.
var ErrTooLarge = errors.New("bytes.Buffer: too large")
var errNegativeRead = errors.New("bytes.Buffer: reader returned negative count from Read")

const maxInt = int(^uint(0) >> 1)

// Bytes returns a slice of length b.Len() holding the unread portion of the buffer.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
def main():
    def iterfile():  # (1)
        with open(some_file_path, mode="rb") as file_like:  # (2)
            yield from file_like  # (3)

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.