if (path == null) {
            logger.warn("{} was not found.", path);
            return null;
        }
        final File osddFile = new File(path);
        if (!osddFile.isFile()) {
            logger.warn("{} was not a file.", path);
            return null;
        }
        return osddFile;
    }

    protected boolean isOsddLinkEnabled() {

    String ROLE = Mojo.class.getName();

    /**
     * Perform whatever build-process behavior this <code>Mojo</code> implements.<br>
     * This is the main trigger for the <code>Mojo</code> inside the <code>Maven</code> system, and allows
     * the <code>Mojo</code> to communicate errors.
     *
     * @throws MojoExecutionException if an unexpected problem occurs.

    }

    private
    fun configureKotlinCompilerIoForWindowsSupport() =
        org.jetbrains.kotlin.cli.common.environment.setIdeaIoUseFallback()
}


private
inline fun <T : Any?> Disposable.use(action: Disposable.() -> T) =
    try {
        action(this)
    } finally {
        Disposer.dispose(this)

# Body - Fields

The same way you can declare additional validation and metadata in *path operation function* parameters with `Query`, `Path` and `Body`, you can declare validation and metadata inside of Pydantic models using Pydantic's `Field`.

## Import `Field`

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

  env | grep ^TFCI_ > "$env_file"

  WORKING_DIR="$TFCI_GIT_DIR"
  if [[ `uname -s | grep -P '^MSYS_NT'` ]]; then
    env_file=$(cygpath -m $env_file)
    # Host dirs can only be mapped to an existing drive inside the container, so
    # T:\ is replaced with C:\.
    _TFCI_OUTPUT_DIR_WIN=$(replace_drive_letter_with_c "$TFCI_OUTPUT_DIR")
    sed -iE 's|^TFCI_OUTPUT_DIR=.*|TFCI_OUTPUT_DIR='"$_TFCI_OUTPUT_DIR_WIN"'|g' $env_file

          cache-from: type=registry,ref=tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-to: type=inline
      -
        name: Image digest

                packageName = annotation.getMemberValue("pn")?.stringValue ?: "",
                extraInt = annotation.getMemberValue("xi")?.intValue ?: 0
            )
        }

    private
    inline fun <reified T : Any> CtClass.ctAnnotation(): Annotation? =
        getAnnotation(T::class.java)
            ?.takeIf { Proxy.isProxyClass(it::class.java) }
            ?.let { Proxy.getInvocationHandler(it) as? AnnotationImpl }

                                            <la:form action="/admin/backup/upload/" enctype="multipart/form-data"
                                                     styleClass="form-inline">
                                                <label for="bulkFile" class="mb-2 mr-2"><la:message
                                                        key="labels.backup_bulk_file"/></label>

        String path = getPathForLocalArtifact(request.getArtifact());
        File file = new File(getRepository().getBasedir(), path);

        LocalArtifactResult result = new LocalArtifactResult(request);
        if (file.isFile()) {
            result.setFile(file);
            result.setAvailable(true);
        }

        return result;
    }

Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.