// If `dx` is nullptr, the implementation will use dx of `OnesLike` for all
// shapes in `y`.
// The partial derivatives are returned in `dy`. `dy` should be allocated to
// size `nx`.
// `prefix` names the scope into which all gradients operations are being added.
// `prefix` must be unique within the provided graph otherwise this operation
// will fail. If `prefix` is nullptr, the default prefixing behaviour takes

    /** The text content field. */
    public static final String TEXT = "text";
    /** The prefix for reading fields. */
    public static final String READING_PREFIX = "reading_";
    /** The score field. */
    public static final String SCORE = "score";
    /** The query frequency field. */
    public static final String QUERY_FREQ = "queryFreq";
    /** The document frequency field. */
    public static final String DOC_FREQ = "docFreq";

 *
 * <p>Usage example:</p>
 * <pre>
 * {@code
 * DefaultContentsParser parser = new DefaultContentsParser();
 * SuggestItem item = parser.parseSearchWords(words, readings, fields, tags, roles, score, readingConverter, normalizer, analyzer, langs);
 * }
 * </pre>
 *
 * @see ContentsParser
 * @see SuggestItem
 * @see ReadingConverter
 * @see Normalizer
 * @see SuggestAnalyzer
 */

     * @param fields the array of fields associated with the search words
     * @param tags the array of tags associated with the search words
     * @param roles the array of roles associated with the search words
     * @param score the score associated with the search words
     * @param readingConverter the converter to use for converting readings
     * @param normalizer the normalizer to use for normalizing the search words

    "kinds": {
      "type": "keyword"
    },
    "queryFreq": {
      "type": "long"
    },
    "roles": {
      "type": "keyword"
    },
    "languages": {
      "type": "keyword"
    },
    "score": {
      "type": "double"
    },
    "tags": {
      "type": "keyword"
    },
    "text": {
      "type": "keyword"
    },
    "userBoost": {
      "type": "double"
    }
  }

            builder.setSize(size * 2);
        } else {
            builder.setSize(size);
        }

        // set query.
        final QueryBuilder q = buildQuery(query, fields);

        // set function score
        final QueryBuilder queryBuilder = buildFunctionScoreQuery(query, q);
        builder.addSort("_score", SortOrder.DESC);

        // set filter query.

        map.put(FieldNames.ROLES, new String[] {});
        map.put(FieldNames.LANGUAGES, new String[] {});
        map.put(FieldNames.KINDS, new String[] {});
        map.put(FieldNames.SCORE, 1.0F);
        map.put(FieldNames.QUERY_FREQ, 0L);
        map.put(FieldNames.DOC_FREQ, 0L);
        map.put(FieldNames.USER_BOOST, 1.0F);

    if [[ ! -s $BATS_TEST_TMPDIR/files ]]; then return 0; fi
    xargs -a $BATS_TEST_TMPDIR/files -n1 -P $(nproc --all) \
        python -m pylint --rcfile=tensorflow/tools/ci_build/pylintrc --score false \
        | grep -v "**** Module" \
        | tee $BATS_TEST_TMPDIR/needs_help.txt
    [[ ! -s $BATS_TEST_TMPDIR/needs_help.txt ]]
}

        assertEquals(25L, source.get(FieldNames.DOC_FREQ));
        assertEquals(2.0f, source.get(FieldNames.USER_BOOST));
        assertEquals(150.0f, source.get(FieldNames.SCORE)); // (50 + 25) * 2.0 = 150

        assertNotNull(source.get(FieldNames.READING_PREFIX + "0"));
        assertNotNull(source.get(FieldNames.READING_PREFIX + "1"));

func (p *Parser) asmText(operands [][]lex.Token) {
	if len(operands) != 2 && len(operands) != 3 {
		p.errorf("expect two or three operands for TEXT")
		return
	}

	// Labels are function scoped. Patch existing labels and
	// create a new label space for this TEXT.
	p.patch()
	p.labels = make(map[string]*obj.Prog)

	// Operand 0 is the symbol name in the form foo(SB).