case bucketVersioningConfig:
				config, _, err := globalBucketMetadataSys.GetVersioningConfig(bucket)
				if err != nil {
					writeErrorResponse(ctx, w, exportError(ctx, err, cfgFile, bucket), r.URL)
					return
				}
				// ignore empty versioning configs
				if config.Status != versioning.Enabled && config.Status != versioning.Suspended {
					continue
				}

        QueryFieldConfig config = new QueryFieldConfig();
        assertNotNull(config);

        // Verify initial state (fields should be null before init)
        assertNull(config.responseFields);
        assertNull(config.scrollResponseFields);
        assertNull(config.cacheResponseFields);
        assertNull(config.highlightedFields);
        assertNull(config.searchFields);

    ],
)

# Config setting that is satisfied when TensorFlow is being built with CUDA
# support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS).
alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_config_cuda",
    ),
)

}

// loadFormatErasureAll - load all format config from all input disks in parallel.
func loadFormatErasureAll(storageDisks []StorageAPI, heal bool) ([]*formatErasureV3, []error) {
	// Initialize list of errors.
	g := errgroup.WithNErrs(len(storageDisks))

	// Initialize format configs.
	formats := make([]*formatErasureV3, len(storageDisks))

	// Load format from each disk in parallel

import common.uuidPrefix
import jetbrains.buildServer.configs.kotlin.Dependencies
import jetbrains.buildServer.configs.kotlin.DslContext
import jetbrains.buildServer.configs.kotlin.FailureAction
import jetbrains.buildServer.configs.kotlin.RelativeId
import jetbrains.buildServer.configs.kotlin.SnapshotDependency
import jetbrains.buildServer.configs.kotlin.Triggers
import jetbrains.buildServer.configs.kotlin.triggers.ScheduleTrigger

import jetbrains.buildServer.configs.kotlin.AbsoluteId
import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.BuildSteps
import jetbrains.buildServer.configs.kotlin.BuildType
import jetbrains.buildServer.configs.kotlin.BuildTypeSettings
import jetbrains.buildServer.configs.kotlin.CheckoutMode
import jetbrains.buildServer.configs.kotlin.Dependencies

# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

        PropertyConfiguration config = new PropertyConfiguration(props);

        assertTrue(config.isUseMultiChannel());
        assertEquals(6, config.getMaxChannels());
        assertEquals(2, config.getChannelBindingPolicy());
        assertEquals("least_loaded", config.getLoadBalancingStrategy());
        assertEquals(15, config.getChannelHealthCheckInterval());
    }

    @Test

import common.javaHome
import common.requiresOs
import common.toCapitalized
import jetbrains.buildServer.configs.kotlin.AbsoluteId
import jetbrains.buildServer.configs.kotlin.BuildType
import jetbrains.buildServer.configs.kotlin.ParameterDisplay
import jetbrains.buildServer.configs.kotlin.buildSteps.exec
import jetbrains.buildServer.configs.kotlin.triggers.finishBuildTrigger
import promotion.FINAL_RELEASE_BUILD_CONFIGURATION_ID

import jetbrains.buildServer.configs.kotlin.AbsoluteId
import jetbrains.buildServer.configs.kotlin.BuildSteps
import jetbrains.buildServer.configs.kotlin.DslContext
import jetbrains.buildServer.configs.kotlin.buildSteps.GradleBuildStep

fun DslContext.initForTest() {
    // Set the project id here, so we can use methods on the DslContext
    parentProjectId = AbsoluteId("Gradle")
    projectId = AbsoluteId("Gradle_Master")