api(libs.conscrypt.openjdk)
  api(libs.openjsse)

  api(rootProject.libs.junit.jupiter.engine)

  // This runs Corretto on macOS (aarch64) and Linux (x86_64). We don't test Corretto on other
  // operating systems or architectures.
  api(variantOf(libs.amazonCorretto) {
    classifier(
      when {
        OperatingSystem.current().isMacOsX -> "osx-aarch_64"
        OperatingSystem.current().isLinux -> "linux-x86_64"

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.
name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

 *
 * <p>This package contains the implementation of the Maven upgrade tool (mvnup) that helps
 * upgrade Maven projects to be compatible with Maven 4. The tool is organized around
 * a goal-based architecture with pluggable upgrade strategies.</p>
 *
 * <h2>Architecture Overview</h2>
 *
 * <h3>Goals</h3>
 * <ul>
 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.Check} - Analyzes projects and reports needed upgrades</li>

```bash
# Generate code coverage reports
mvn jacoco:report

# License header validation
mvn apache-rat:check

# API compatibility checking
mvn clirr:check
```

### Architecture Overview

The library follows a layered architecture:

- **Context Layer**: `CIFSContext` interface provides the main entry point
- **Resource Layer**: `SmbResource` interface represents SMB network resources