# shellcheck disable=SC2319
    IN_PROW="$?"

    # Don't persist large diffs (30M+) on CI
    LARGE_FILE="$(find "${ARTIFACTS}" -name "${PATCH_NAME}" -type 'f' -size +30M)"
    if [ "${IN_PROW}" -eq 0 ] && [ -n "${LARGE_FILE}" ]; then
      rm "${PATCH_OUT}"
      echo "WARNING: patch file was too large to persist ($(du -h "${PATCH_OUT}"))"
      return 0
    fi

from fastapi import FastAPI
from fastapi.responses import FileResponse

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


@app.get("/", response_class=FileResponse)
async def main():

from docs_src.custom_response import tutorial008
from docs_src.custom_response.tutorial008 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial008.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

      assertThrows<IllegalArgumentException> {
        checkDuration(
          "timeout",
          1L + Int.MAX_VALUE.toLong(),
          TimeUnit.MILLISECONDS,
        )
      },
    ).hasMessage("timeout too large")
  }

  @Test
  fun testDurationDuration() {
    assertThat(checkDuration("timeout", 0.milliseconds)).isEqualTo(0)
    assertThat(checkDuration("timeout", 1.milliseconds)).isEqualTo(1)

    assertThat(

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

	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 docs_src.custom_response import tutorial009
from docs_src.custom_response.tutorial009 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial009.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

.table-warning:hover {\n  background-color: #ffe8a1;\n}\n\n.table-hover .table-warning:hover > td,\n.table-hover .table-warning:hover > th {\n  background-color: #ffe8a1;\n}\n\n.table-danger,\n.table-danger > th,\n.table-danger > td {\n  background-color: #f5c6cb;\n}\n\n.table-danger th,\n.table-danger td,\n.table-danger thead th,\n.table-danger tbody + tbody {\n  border-color: #ed969e;\n}\n\n.table-hover .table-danger:hover {\n  background-color: #f1b0b7;\n}\n\n.table-hover .table-danger:hover > td,\n.table-hover...

Dann gehen Sie zur Theke 🔀, zur ursprünglichen Aufgabe, die nun erledigt ist ⏯, nehmen die Burger auf, sagen Danke, und bringen sie zum Tisch. Damit ist dieser Schritt / diese Aufgabe der Interaktion mit der Theke abgeschlossen ⏹. Das wiederum schafft eine neue Aufgabe, „Burger essen“ 🔀 ⏯, aber die vorherige Aufgabe „Burger holen“ ist erledigt ⏹.

### Parallele Hamburger

.table-warning:hover {\n  background-color: #ffe8a1;\n}\n\n.table-hover .table-warning:hover > td,\n.table-hover .table-warning:hover > th {\n  background-color: #ffe8a1;\n}\n\n.table-danger,\n.table-danger > th,\n.table-danger > td {\n  background-color: #f5c6cb;\n}\n\n.table-danger th,\n.table-danger td,\n.table-danger thead th,\n.table-danger tbody + tbody {\n  border-color: #ed969e;\n}\n\n.table-hover .table-danger:hover {\n  background-color: #f1b0b7;\n}\n\n.table-hover .table-danger:hover > td,\n.table-hover...