inputs.property('acceptedViolations', acceptedViolations.toAcceptedChangesMap())
    inputs.property("baseline.version", compatibilityBaselineVersion)
    inputs.property("currentVersion", baseVersion)
    def apiSourceFolders = configurations.currentSources.incoming.artifactView { lenient(true) }.files
    inputs.files("apiSourceFolders", apiSourceFolders)
    inputs.files(currentClasspath)

        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler/clusters:single_machine",
        "//tensorflow/core/grappler/costs:graph_properties",
        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
        "//tensorflow/core/protobuf:error_codes_proto_impl_cc",
    ],

wrapperUpdateTask("nightly", "nightly")
wrapperUpdateTask("rc", "release-candidate")
wrapperUpdateTask("current", "current")

tasks.withType<Wrapper>().configureEach {
    val jvmOpts = "-Dfile.encoding=UTF-8"
    inputs.property("jvmOpts", jvmOpts)
    doLast {
        val optsEnvVar = "DEFAULT_JVM_OPTS"
        scriptFile.writeText(scriptFile.readText().replace("$optsEnvVar='", "$optsEnvVar='$jvmOpts "))

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

        daemonRegistry = repoRoot().dir("build/daemon")
        gradleSnippetsDir = repoRoot().dir("platforms/documentation/docs/src/snippets") // TODO use dependency management
    }

    // Wire the different inputs for local distributions and repos that are declared by dependencies in the build scripts
    normalizedDistributionZip.distributionZip = configurations["${prefix}TestNormalizedDistributionPath"]

    val generatorRoot = "$projectDir/analysis/analysis-api-fir/analysis-api-fir-generator/src/"

    val generatorConfigurationFiles = fileTree(generatorRoot) {
        include("**/*.kt")
    }

    inputs.files(generatorConfigurationFiles)

    workingDir = rootDir
    classpath = generatorClasspath
    mainClass.set("org.jetbrains.kotlin.analysis.api.fir.generator.MainKt")
    systemProperties["line.separator"] = "\n"

       - This is unfortunate, because actually it would be safe to declare
       the task as up-to-date, because these two files, which are based on
       the generated index.xml, are outputs, not inputs. We can be sure of
       this because they are deleted in the @BeforeEach method of the
       OsgiTest test class.

       - To enable the benefit of incremental builds, we can ask Gradle

# Controllers

Istio has a variety of [controllers](https://kubernetes.io/docs/concepts/architecture/controller/), which basically watch some inputs and do something.
This can be reading from Kubernetes and writing other objects back, writing to proxies over XDS, etc.

Unfortunately, writing controllers is very error prone, even for seemingly simple cases.
To work around this, Istio has a variety of abstractions meant to make writing controllers easier.

## Clients

/**
 * Tests that check the end-to-end behavior of a Gradle distribution.
 * They can have a locally built Gradle distribution on their runtime classpath
 * and distributions as well as local repositories as additional test inputs
 * to test functionality that requires rea distributions (like the wrapper)
 * or separately published libraries (like the Tooling API Jar).
 */

### Usage

```
minio server --config config.yaml
```

Lets you start MinIO server with all inputs to start MinIO server provided via this configuration file, once the configuration file is provided all other pre-existing values on disk for configuration are overridden by the new values set in this configuration file.