public class ChecksumBenchmark {

  // Use a constant seed for all of the benchmarks to ensure apples to apples comparisons.
  private static final int RANDOM_SEED = new Random().nextInt();

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    new Random(RANDOM_SEED).nextBytes(testBytes);
  }

  // CRC32

    environ_cp['TF_NEED_CUDA'] = '0'
  else:
    environ_cp['TF_NEED_CUDA'] = str(
        int(get_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)))
  if (environ_cp.get('TF_NEED_CUDA') == '1' and
      'TF_CUDA_CONFIG_REPO' not in environ_cp):

    set_action_env_var(
        environ_cp,
        'TF_NEED_TENSORRT',
        'TensorRT',
        False,
        bazel_config_name='tensorrt')

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

    implementation(project(":analysis:analysis-api"))

    implementation(commonDependency("org.jetbrains.kotlin:kotlin-reflect")) { isTransitive = false }

    /*
     We do not need guava in the generator, but because of a bug in the IJ project importing, we need to have a dependency on intellijCore
     the same as it is in `:fir:tree:tree-generator` module to the project be imported correctly
     */
    compileOnly(intellijCore())

func checkExistingKubeConfigFile(cfg *config.InstallConfig, expectedKC kubeconfig) error {
	kubeconfigFilepath := filepath.Join(cfg.MountedCNINetDir, cfg.KubeconfigFilename)

	existingKC, err := os.ReadFile(kubeconfigFilepath)
	if err != nil {
		installLog.Debugf("no preexisting kubeconfig at %s, assuming we need to create one", kubeconfigFilepath)
		return err
	}

import org.apache.maven.plugin.descriptor.MojoDescriptor;

// TODO lifecycles being executed
// TODO what runs in each phase
// TODO plugins that need downloading
// TODO project dependencies that need downloading
// TODO unfortunately the plugins need to be downloaded in order to get the plugin.xml file. need to externalize this
//      from the plugin archive.
// TODO this will be the class that people get in IDEs to modify

/**

      handleAllCompleted();
      return;
    }

    // NOTE: If we ever want to use a custom executor here, have a look at CombinedFuture as we'll
    // need to handle RejectedExecutionException

    if (allMustSucceed) {
      // We need fail fast, so we have to keep track of which future failed so we can propagate
      // the exception immediately

		// We have no good way to distinguish between these two cases from here. But we don't need to!
		// Existing pods will be handled by the dataplane using `GetAmbientPods`,
		// and the initial enqueueNamespace, and new pods will be handled by the CNI.

	case controllers.EventUpdate:
		// For update, we just need to handle opt outs
		newPod := event.New.(*corev1.Pod)
		oldPod := event.Old.(*corev1.Pod)

      required: true

  - type: textarea
    attributes:
      label: How do you want it to be improved?
    validations:
      required: true

  - type: textarea
    attributes:
      label: Why do we need it to be improved?
    validations:
      required: true

  - type: textarea
    attributes:
      label: Example
      description: >

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.