import org.apache.maven.api.PathType;
import org.apache.maven.api.Project;
import org.apache.maven.api.RemoteRepository;
import org.apache.maven.api.Session;
import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Immutable;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.annotations.NotThreadSafe;
import org.apache.maven.api.annotations.Nullable;

/**

      val end = cursor.end
      if (buffer != null) {
        while (i < end) {
          keyIndex %= keyLength // Reassign to prevent overflow breaking counter.

          // Byte xor is experimental in Kotlin so we coerce bytes to int, xor them
          // and convert back to byte.
          val bufferInt: Int = buffer[i].toInt()
          val keyInt: Int = key[keyIndex].toInt()

    EXPERIMENTAL_VFS_RETENTION("Experimental FS Watching", "On demand checks to run tests with file system watching enabled", "ExperimentalVfsRetention"),
    EXPERIMENTAL_PERFORMANCE("Experimental Performance", "Try out new performance test running", "ExperimentalPerformance");

    val id: String

		Long: `
Checks associated resources of the given resource, and running webhooks to examine whether the pod can be or will be injected or not.`,
		Example: `  # Check the injection status of a pod
  istioctl experimental check-inject details-v1-fcff6c49c-kqnfk.test
	
  # Check the injection status of a pod under a deployment
  istioctl x check-inject deployment/details-v1

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_unified_experimental.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,

import javax.tools.StandardLocation;

import java.io.File;
import java.nio.file.Path;
import java.util.Objects;
import java.util.Optional;
import java.util.StringJoiner;

import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Nonnull;

/**
 * The option of a Java command-line tool where to place the paths to some dependencies.

**`krt`** is a very high level wrapper around Kubernetes informers, building on top of `kclient`.

See the [krt README](../../pkg/kube/krt/README.md) for more information.

*krt is currently experimental; please ask maintainers before utilizing it in new areas.*

## Writing Controllers

The `controllers` package offers a variety of helpers for writing controllers.

#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA

  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kEager || ptr->getKind() == kTfrt;
  }

  //===--------------------------------------------------------------------===//
  // Experimental Custom Device.
  //===--------------------------------------------------------------------===//
  virtual CustomDeviceOpHandler& GetCustomDeviceOpHandler() = 0;