lop\":0,\"zero_terms_query\":\"NONE\",\"boost\":0.5}}},{\"fuzzy\":{\"title\":{\"value\":\"QUERY1\",\"fuzziness\":\"AUTO\",\"prefix_length\":0,\"max_expansions\":10,\"transpositions\":true,\"boost\":0.01}}},{\"fuzzy\":{\"content\":{\"value\":\"QUERY1\",\"fuzziness\":\"AUTO\",\"prefix_length\":0,\"max_expansions\":10,\"transpositions\":true,\"boost\":0.005}}}],\"adjust_pure_negative\":true,\"boost\":1.0}},\"functions\":[{\"filter\":{\"match_all\":{\"boost\":1.0}},\"field_value_factor\":{\"field\":...

                            .maxExpansions(fessConfig.getQueryFuzzyExpansionsAsInteger())
                            .prefixLength(fessConfig.getQueryFuzzyPrefixLengthAsInteger())
                            .transpositions(Constants.TRUE.equalsIgnoreCase(fessConfig.getQueryFuzzyTranspositions())));
        }

        if (isSearchField(field)) {
            final String text = term.text();
            context.addFieldLog(field, text);

                "xxx:aaa");

            assertTrue(result instanceof FuzzyQueryBuilder);
            FuzzyQueryBuilder fqb = (FuzzyQueryBuilder) result;
            assertEquals(boost, fqb.boost());

        }
    }

    // Test with transpositions enabled
    public void test_convertFuzzyQuery_withTranspositionsEnabled() {
        QueryContext context = new QueryContext("test", false);
        Term term = new Term("title", "fuzzy");

    Direct batched convolution Pooling: maximum, minimum, average Normalization:
    LRN, batch normalization Activation: rectified linear unit (ReLU) Data
    manipulation: multi-dimensional transposition (conversion), split, concat,
    sum and scale.

*   TensorForest Estimator now supports SavedModel export for serving.

*   Support client-provided ClusterSpec's and propagate them to all workers to