with path.open("rb") as file:
        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


def test_post_large_file(tmp_path):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    client = TestClient(app)
    with path.open("rb") as file:

	SRLIW	$-1, X5, X6			// ERROR "immediate out of range 0 to 31"
	SRAIW	$-1, X5, X6			// ERROR "immediate out of range 0 to 31"
	SD	X5, 4294967296(X6)		// ERROR "constant 4294967296 too large"
	SRLI	$1, X5, F1			// ERROR "expected integer register in rd position but got non-integer register F1"
	SRLI	$1, F1, X5			// ERROR "expected integer register in rs1 position but got non-integer register F1"

      description="MinIO object storage is fundamentally different. Designed for performance and the S3 API, it is 100% open-source. MinIO is ideal for large, private cloud environments with stringent security requirements and delivers mission-critical availability across a diverse range of workloads."

ENV MINIO_ACCESS_KEY_FILE=access_key \
    MINIO_SECRET_KEY_FILE=secret_key \

// corresponding increase or decrease in Mantissa) such that:
//
// - No precision is lost
// - No fractional digits will be emitted
// - The exponent (or suffix) is as large as possible.
//
// The sign will be omitted unless the number is negative.
//
// Examples:
//
// - 1.5 will be serialized as "1500m"
// - 1.5Gi will be serialized as "1536Mi"
//

			return fmt.Errorf("Default retention period too large for 'Days' %d", *retention.Days)
		}
	} else if *retention.Years == 0 {
		return fmt.Errorf("Default retention period must be a positive integer value for 'Years'")
	} else if *retention.Years > maximumRetentionYears {
		return fmt.Errorf("Default retention period too large for 'Years' %d", *retention.Years)
	}

	*dr = DefaultRetention(retention)

* 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.

        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


@needs_py39
def test_post_large_file(tmp_path, client: TestClient):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    with path.open("rb") as file:

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

    with path.open("rb") as file:
        response = client.post("/files/", files={"file": file})
    assert response.status_code == 200, response.text
    assert response.json() == {"file_size": 14}


def test_post_large_file(tmp_path):
    default_pydantic_max_size = 2**16
    path = tmp_path / "test.txt"
    path.write_bytes(b"x" * (default_pydantic_max_size + 1))

    client = TestClient(app)
    with path.open("rb") as file:

  // the list automatically by PodDisruptionBudget controller after some time.
  // If everything goes smooth this map should be empty for the most of the time.
  // Large number of entries in the map may indicate problems with pod deletions.
  // +optional
  map<string, k8s.io.apimachinery.pkg.apis.meta.v1.Time> disruptedPods = 2;

  // Number of pod disruptions that are currently allowed.