return ItemOut(name=item.name, value=item.value, dep=dep)


@app.post("/sync/large-receive")
def sync_large_receive(payload: LargeIn):
    return {"received": len(payload.items)}


@app.post("/async/large-receive")
async def async_large_receive(payload: LargeIn):
    return {"received": len(payload.items)}


@app.get("/sync/large-dict-no-response-model")
def sync_large_dict_no_response_model():

            final Map<String, Object> doc = new HashMap<>();
            doc.put("title", NAME_PREFIX + "large_" + i);
            doc.put("url", "http://example.com/bulk/large/" + i);
            doc.put("content", "This is test content for large bulk document " + i);
            doc.put("boost", 1.0f);
            doc.put("role", "Rguest");
            documents.add(doc);
        }

    }

    /**
     * Test readBytes with large file throws exception
     *
     * @throws Exception
     */
    @Test
    public void testReadBytes_LargeFile() throws Exception {
        final File largeFile = tempFolder.newFile("large.dat");

        // Create a file larger than MAX_BUF_SIZE (10MB)
        // Write 11MB of data

            if (fileSize > maxSize) {
                throw new IORuntimeException(new IOException(
                        "File too large: " + fileSize + " bytes (max: " + maxSize + " bytes). Use streaming APIs for large files."));
            }

            final ByteBuffer buffer = ByteBuffer.allocate((int) fileSize);
            ChannelUtil.read(channel, buffer);

        }
    }

    @Test
    public void test_execute_withVeryLargeBoost() {
        // Test executing BoostQuery with very large boost value
        Term term = new Term("field", "large");
        TermQuery termQuery = new TermQuery(term);
        BoostQuery boostQuery = new BoostQuery(termQuery, Float.MAX_VALUE);

        QueryContext context = new QueryContext("test", false);

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)

        String result3 = aggregateLogJob.execute();
        assertEquals("", result3);
    }

    @Test
    public void test_execute_performanceWithLargeErrorMessage() {
        // Test with very large error message
        final StringBuilder largeMessage = new StringBuilder();
        for (int i = 0; i < 1000; i++) {
            largeMessage.append("Error detail ").append(i).append(" ");
        }

	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.

* 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.
* It has a [file-like](https://docs.python.org/3/glossary.html#term-file-like-object) `async` interface.

from fastapi import FastAPI
from fastapi.responses import FileResponse

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


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