- PDF documents (text and metadata extraction)
- Images (JPEG, PNG, GIF, TIFF, BMP)
- Image metadata (EXIF, IPTC, XMP)

#### Archives and Compressed Files
- ZIP, TAR, GZ archives
- LHA compression format
- Nested archive extraction

#### Web and Markup
- HTML, XHTML with XPath support
- XML documents
- JSON and structured data

#### Media Files
- Audio formats (MP3, WAV, FLAC)
- Video formats (MP4, AVI, MOV)

	tok := in.Stack.Next()
	if tok != scanner.Ident {
		in.expectText("expected identifier after '#'")
	}
	if !in.enabled() {
		// Can only start including again if we are at #else or #endif but also
		// need to keep track of nested #if[n]defs.
		// We let #line through because it might affect errors.
		switch in.Stack.Text() {
		case "else", "endif", "ifdef", "ifndef", "line":
			// Press on.
		default:
			return false
		}

 * and manage various types of settings such as array settings, analyzer settings, bad word settings,
 * and elevate word settings.</p>
 *
 * <p>It also includes a nested TimeoutSettings class to manage various timeout configurations.</p>
 *
 * <p>Usage example:</p>
 * <pre>
 * {@code
 * Client client = ...;
 * Map<String, Object> initialSettings = ...;

// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
absl::Status ProcessInputs(
    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,

 *
 * This class provides methods to normalize text by reading characters from a provided Reader
 * and processing them according to specific rules. The main functionality is encapsulated
 * within the nested {@link TextNormalizeContext} class.
 *
 * <p>The text normalization process includes:
 * <ul>
 *   <li>Treating ISO control characters and specified space characters as spaces.</li>

        assertNotNull(queryList);
        assertEquals(2, queryList.size());
    }

    @Test
    public void testGetTermQueryListWithNestedBooleanQuery() {
        // Test with nested BooleanQuery
        BooleanQuery.Builder innerBuilder = new BooleanQuery.Builder();
        innerBuilder.add(new TermQuery(new Term("content", "inner")), BooleanClause.Occur.SHOULD);

    import.
*   Breaking change to `tf.contrib.data.Dataset` APIs that expect a nested
    structure. Lists are now converted to `tf.Tensor` implicitly. You may need
    to change uses of lists to tuples in existing code. In addition, dicts are
    now supported as a nested structure.

## Changes to contrib APIs

# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",
    define_values = {"no_xla_deps_in_cuda": "true"},
    visibility = ["//visibility:public"],
)

# Crosses between framework_shared_object and a bunch of other configurations
# due to limitations in nested select() statements.
config_setting(
    name = "framework_shared_object",

<a href="#Interface_types">interface type</a>, but not a
<a href="#Type_parameter_declarations">type parameter</a>, and each non-interface type
<code>T</code> listed in a case must implement the type of <code>x</code>.
The types listed in the cases of a type switch must all be
<a href="#Type_identity">different</a>.
</p>

<pre class="ebnf">

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --enable_platform_specific_config

# Enable XLA support by default.
build --define=with_xla_support=true

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --config=short_logs

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --config=v2