Name:   "compat",
		Usage:  "enable strict S3 compatibility by turning off certain performance optimizations",
		Hidden: true,
	},
	// This flag is hidden and to be used only during certain performance testing.
	cli.BoolFlag{
		Name:   "no-compat",
		Usage:  "disable strict S3 compatibility by turning on certain performance optimizations",
		Hidden: true,
	},
}

// Help template for minio.
var minioHelpTemplate = `NAME:

	for i := range listener.tcpListeners {
		addrs = append(addrs, listener.tcpListeners[i].Addr())
	}

	return addrs
}

// TCPOptions specify customizable TCP optimizations on raw socket
type TCPOptions struct {
	UserTimeout        int              // this value is expected to be in milliseconds

#### Optimizations

A naive implementation would simply regenerate all resources, of all subscribed types, for each client, on any configuration change. However, this scales poorly. As a result, we have many levels of optimizations to avoid doing this work.

In all these examples, the solution is the same:
use explicit synchronization.
</p>

<h2 id="badcompiler">Incorrect compilation</h2>

<p>
The Go memory model restricts compiler optimizations as much as it does Go programs.
Some compiler optimizations that would be valid in single-threaded programs are not valid in all Go programs.
In particular, a compiler must not introduce writes that do not exist in the original program,

   * be updated after modifications to {@code graph}.
   *
   * @since 33.1.0 (present with return type {@code Graph} since 20.0)
   */
  // TODO(b/31438252): Consider potential optimizations for this algorithm.
  public static <N> ImmutableGraph<N> transitiveClosure(Graph<N> graph) {
    ImmutableGraph.Builder<N> transitiveClosure =
        GraphBuilder.from(graph).allowsSelfLoops(true).<N>immutable();

            optimizations. Setting the variable to 0 or unsetting it will
            disable the optimizations.
        *   These optimizations can yield slightly different numerical results
            from when they are off due to floating-point round-off errors from
            different computation approaches and orders.
        *   To verify that the optimizations are on, look for a message with

#### Debug builds

When [building Tensorflow](https://www.tensorflow.org/install/source), passing
`--config=dbg` to Bazel will build with debugging information and without
optimizations, allowing you to use GDB or other debuggers to debug C++ code. For
example, you can build the pip package with debugging information by running:

```bash

build:cuda --@local_config_cuda//:enable_cuda

# CUDA: This config refers to building CUDA op kernels with clang.
build:cuda_clang --config=cuda
# Enable TensorRT optimizations https://developer.nvidia.com/tensorrt
build:cuda_clang --config=tensorrt
build:cuda_clang --action_env=TF_CUDA_CLANG="1"
build:cuda_clang --@local_config_cuda//:cuda_compiler=clang

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

But by following the steps above, it will be able to do some performance optimizations.

## Technical Details

Modern versions of Python have support for **"asynchronous code"** using something called **"coroutines"**, with **`async` and `await`** syntax.

     * system defaults will be used.
     *
     * Most applications should not call this method, and instead use the system defaults. Those
     * classes include special optimizations that can be lost if the implementations are decorated.
     *
     * If necessary, you can create and configure the defaults yourself with the following code:
     *
     * ```java