interpreted as a boolean indicator, assume that the user has made a
      scripting error, and will continue to provide invalid input.
      Raise the error to avoid infinitely looping.
  """
  if not question:
    question = 'Do you wish to build TensorFlow with {} support?'.format(
        query_item)
  if not yes_reply:

~~ under the License.

 -----
 Maven Logging
 -----
 Hervé Boutemy
 -----
 2013-08-02
 -----

Maven Logging

 {{{/maven-logging.html}End-user logging documentation}} is available {{{/maven-logging.html}in Maven site}}.
 This documentation is focused on internal implementation details.

* Logging API

 Maven uses

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL
        logging. You can also use `tf.keras.utils.enable_interactive_logging()`

	logger.LogIf(ctx, "tier", err, errKind...)
}

func kmsLogIf(ctx context.Context, err error, errKind ...any) {
	logger.LogIf(ctx, "kms", err, errKind...)
}

// KMSLogger permits access to kms module specific logging
type KMSLogger struct{}

// LogOnceIf is the implementation of LogOnceIf, accessible using the Logger interface
func (l KMSLogger) LogOnceIf(ctx context.Context, err error, id string, errKind ...any) {

      {
        "subprojects": [
          "declarative-dsl-provider",
          "declarative-dsl-tooling-builders",
          "enterprise",
          "execution-e2e-tests",
          "file-collections",
          "ide-plugins",
          "integ-test",
          "ivy",
          "jacoco",
          "kotlin-dsl-tooling-builders",
          "language-groovy",
          "maven",
          "platform-native",

4. The daemon runs the request, sending back data such as logging output or tooling API events and intermediate models while doing so.
5. The daemon sends the result back. For some requests, this might be a simple success/failure result, and for others this might also include a more complex object, such as a tooling API model.

  },
  {
    "name": "declarative-dsl-tooling-builders",
    "path": "platforms/core-configuration/declarative-dsl-tooling-builders",
    "unitTests": false,
    "functionalTests": false,
    "crossVersionTests": true
  },
  {
    "name": "declarative-dsl-tooling-models",
    "path": "platforms/core-configuration/declarative-dsl-tooling-models",
    "unitTests": false,
    "functionalTests": false,

h3. Plugins

* Execution model
* Metadata model
* Metadata tooling
 ** Metadata extractor
 ** Metadata reader
 ** Metadata writer
 ** Metadata adapter (if required for the target system)
* Maven packaging and lifecycle
* Maven test harness for plugin execution model
* Eclipse IDE tooling for plugin execution model and metadata model

- we also seem to have information like the plugin lifecycle model that's buried inside the maven execution model

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 233
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",

    "groups" : [ {
      "testProject" : "excludeRuleMergingBuild",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
    "groups" : [ {