healthCheckClusterPath     = "/cluster"
	healthCheckClusterReadPath = "/cluster/read"
	healthCheckPathPrefix      = minioReservedBucketPath + healthCheckPath
)

// registerHealthCheckRouter - add handler functions for liveness and readiness routes.
func registerHealthCheckRouter(router *mux.Router) {
	// Healthcheck router
	healthRouter := router.PathPrefix(healthCheckPathPrefix).Subrouter()

class STSElement(object):
    """STS aware XML parsing class. Wraps a root element name and
    cElementTree.Element instance. Provides STS namespace aware parsing
    functions.

    """

    def __init__(self, root_name, element):
        self.root_name = root_name
        self.element = element

    @classmethod
    def fromstring(cls, root_name, data):

    return {"fresh_value": fresh_value}
```

////

## Recap

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

)

type prometheusAuthType string

const (
	prometheusJWT    prometheusAuthType = "jwt"
	prometheusPublic prometheusAuthType = "public"
)

// registerMetricsRouter - add handler functions for metrics.
func registerMetricsRouter(router *mux.Router) {
	// metrics router
	metricsRouter := router.NewRoute().PathPrefix(minioReservedBucketPath).Subrouter()

   * input element is mapped to one cell in the returned table, with the rows, columns, and values
   * generated by applying the specified functions.
   *
   * <p>The returned {@code Collector} will throw a {@code NullPointerException} at collection time
   * if the row, column, or value functions return null on any input.
   *
   * @since 33.2.0 (available since 21.0 in guava-jre)
   */
  @SuppressWarnings("Java7ApiChecker")

#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {
namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {

We are not restricted to having only one dependency that can return that type of data.

///

## Other models

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import java.util.Random;

/**
 * Benchmarks for comparing the various {@link HashFunction functions} that we provide.
 *
 * <p>Parameters for the benchmark are:
 *
 * <ul>
 *   <li>size: The length of the byte array to hash.
 *   <li>hashFunctionEnum: The {@link HashFunction} to use for hashing.
 * </ul>
 *

`Body` also returns objects of a subclass of `FieldInfo` directly. And there are others you will see later that are subclasses of the `Body` class.

Remember that when you import `Query`, `Path`, and others from `fastapi`, those are actually functions that return special classes.

///

/// tip

  // If true, use TFRT backend
  bool use_tfrt = false;
  // Whether to run elementary eager ops wrapped in a call op.
  bool run_eager_op_as_function = false;
  // Whether to rewrite jit_compile functions.
  bool jit_compile_rewrite = false;
};