mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk
mapreduce.task.sort.spill.percent=0.9 # Minimum % before spilling to disk
```

            }
        }, "Multiple close operations should not cause issues");

        // Verify iterator still works after multiple closes
        assertFalse(emptyIterator.hasNext(), "Iterator should still work after multiple closes");
        assertNull(emptyIterator.next(), "Iterator should still return null after multiple closes");
    }

    @Test

        // Test additional acquire
        session.acquire();
        assertTrue(session.isInUse(), "Session should still be in use after additional acquire");

        // Test release (still in use because of initial transport acquire)
        session.release();
        assertTrue(session.isInUse(), "Session should still be in use after one release");

        // Test final release
        session.release();

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

{* ../../docs_src/dataclasses/tutorial003.py hl[1,5,8:11,14:17,23:25,28] *}

1. We still import `field` from standard `dataclasses`.

            // Execute
            TimeZone result = newProvider.getRequestedTimeZone(requestManager);

            // Verify that the provider still returns the static centralTimeZone
            assertNotNull(result);
            assertEquals(FessUserTimeZoneProcessProvider.centralTimeZone, result);
            assertNotSame(newTimeZone, result);
        } finally {

**FastAPI** is already being used in production in many applications and systems. And the test coverage is kept at 100%. But its development is still moving quickly.

New features are added frequently, bugs are fixed regularly, and the code is still continuously improving.

For example, imagine that you want to return an HTTP status code of "OK" `200` by default.

But if the data didn't exist, you want to create it, and return an HTTP status code of "CREATED" `201`.

But you still want to be able to filter and convert the data you return with a `response_model`.

For those cases, you can use a `Response` parameter.

## Use a `Response` parameter { #use-a-response-parameter }

## Why is Erasure Code useful?

Hiding your documentation user interfaces in production *shouldn't* be the way to protect your API.

That doesn't add any extra security to your API, the *path operations* will still be available where they are.

If there's a security flaw in your code, it will still exist.

Like `item.model_dump(exclude_unset=True)`.

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///