# Using the Request Directly

Up to now, you have been declaring the parts of the request that you need with their types.

Taking data from:

* The path as parameters.
* Headers.
* Cookies.
* etc.

And by doing so, **FastAPI** is validating that data, converting it and generating documentation for your API automatically.

But there are situations where you might need to access the `Request` object directly.

Nils Lindemann <******@****.***> 1711829912 +0100

jaystone776 <******@****.***> 1711838642 +0800

LeeeeT <******@****.***> 1680341164 +0300

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.

Michael Weiner <******@****.***> 1697542898 -0500

// and returns the old value.
// Consider using the more ergonomic and less error-prone [Int32.And] instead.
func AndInt32(addr *int32, mask int32) (old int32)

// AndUint32 atomically performs a bitwise AND operation on *addr using the bitmask provided as mask
// and returns the old value.

  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge1 : TFE_MonitoringGauge<tensorflow::string, 1> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge2 : TFE_MonitoringGauge<tensorflow::string, 2> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge3 : TFE_MonitoringGauge<tensorflow::string, 3> {

using ::mlir::tf_type::HasCompatibleElementTypes;  // NOLINT
using ::mlir::tf_type::IsValidTFTensorType;        // NOLINT
using ::mlir::tf_type::OperandShapeIterator;       // NOLINT
using ::mlir::tf_type::ResourceType;               // NOLINT
using ::mlir::tf_type::ResultShapeIterator;        // NOLINT
using ::mlir::tf_type::ResultShapeRange;           // NOLINT
using ::mlir::tf_type::StringType;                 // NOLINT

        }

        then:
        stdout.toString().contains('this is stdout')
        stdout.toString().contains('A warning using SLF4j')
        stdout.toString().contains('A warning using JCL')
        stdout.toString().contains('A warning using Log4j')
        stdout.toString().contains('A warning using JUL')
        if (targetVersion.baseVersion >= GradleVersion.version('4.7')) {
            // Changed handling of error log messages