line, col int
	Key       string `yaml:"key" json:"key"`
	Value     string `yaml:"value" json:"value"`
}

var _ yaml.Unmarshaler = &BatchJobKV{}

// UnmarshalYAML - BatchJobKV extends default unmarshal to extract line, col information.
func (kv *BatchJobKV) UnmarshalYAML(val *yaml.Node) error {
	type jobKV BatchJobKV
	var tmp jobKV
	err := val.Decode(&tmp)
	if err != nil {
		return err
	}
	*kv = BatchJobKV(tmp)

            Extractor extractor = StringUtil.isBlank(mimeType) ? null : extractorFactory.getExtractor(mimeType);
            if (extractor == null) {
                final String detectedMimeType = getMimeType(out);
                extractor = extractorFactory.getExtractor(detectedMimeType);
                if (extractor == null) {
                    if (logger.isDebugEnabled()) {

    # Enter the output directory.
    mkdir -p "$(dirname "$2")"
    pushd "$(dirname "$2")"

    # Download and extract the binary to the output directory.
    echo "Downloading ${SIDECAR}: $1 to $2"
    time ${DOWNLOAD_COMMAND} --header "${AUTH_HEADER:-}" "$1" |\
      tar --extract --gzip --strip-components=3 --to-stdout > "$2"
    chmod +x "$2"

Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

we can choose to extract and act only the objects within the top-level array:
```go
f, _ := os.Open("input.json")
decoder := jstream.NewDecoder(f, 1) // extract JSON values at a depth level of 1
for mv := range decoder.Stream() {
  fmt.Printf("%v\n ", mv.Value)
}
```

output:
```

        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

		"sum(t)",
		"avg(s.id[1])",
		"coalesce(s.id[1], 2, 2 + 3)",

		"cast(s as string)",
		"cast(s AS INT)",
		"cast(s as DECIMAL)",
		"extract(YEAR from '2018-01-09')",
		"extract(month from '2018-01-09')",

		"extract(hour from '2018-01-09')",
		"extract(day from '2018-01-09')",
		"substring('abcd' from 2 for 2)",
		"substring('abcd' from 2)",
		"substring('abcd' , 2 , 2)",

		"substring('abcd' , 22 )",

# 2. tf_nightly-a.b.c.devYYYYMMDD-py3-none-any.whl
pure_python_whl=$(ls "$TFCI_OUTPUT_DIR"/*py3-none-any*)
pure_python_whl=$(basename "${pure_python_whl}")

# Extract the package name from the wheel name. That is, extract every character
# before the pattern "-py3-" in the wheel name.
pkg_name=$(echo "${pure_python_whl}" | awk -F'-py3-' '{print $1}')

        final Extractor extractor = new Extractor() {
            public ExtractData getText(final InputStream in, final Map<String, String> params) {
                return null;
            }
        };

        assertNull(extractorFactory.getExtractor("test"));
        extractorFactory.addExtractor("test", extractor);
        assertEquals(extractor, extractorFactory.getExtractor("test"));
    }

    /**
     * Transform Graph into a Collection of metadata objects that
     * could serve as a classpath for a particular scope
     *
     * @param dirtyGraph - dependency graph
     * @param scope - which classpath to extract
     * @param resolve - whether to resolve artifacts.
     * @return Collection of metadata objects in the linked subgraph of the graph which
     *             contains the graph.getEntry() vertice
     */