Revert "Require constant() for large constants and fix a type resolving bug (#9769)"

This reverts commit cee1c3a3804d5d3c25407b3c4ac64228e9d194e3.

4 files changed, 0 insertions, 446 deletions

diff --git a/config-model/src/test/derived/neuralnet/neuralnet.sd b/config-model/src/test/derived/neuralnet/neuralnet.sd
deleted file mode 100644
index f916b35cb75..00000000000
--- a/config-model/src/test/derived/neuralnet/neuralnet.sd
+++ /dev/null
@@ -1,238 +0,0 @@
-search neuralnet {
-
-    document neuralnet {
-
-        field pinned type int {
-            indexing: attribute
-        }
-
-        field createdAt type long {
-            indexing: attribute
-        }
-
-        field updatedAt type long {
-            indexing: attribute
-        }
-
-        field uvCount type int {
-            indexing: attribute
-        }
-
-        field dvCount type int {
-            indexing: attribute
-        }
-
-        field aVoteCount type int {
-            indexing: attribute
-        }
-
-        field rCount type int {
-            indexing: attribute
-        }
-
-        field uniqueRACount type int {
-            indexing: attribute
-        }
-
-        field rTo type string {
-            indexing: attribute
-        }
-
-        field markedAsAAt type long {
-            indexing: attribute
-        }
-
-        field normalizedTextScore type float {
-            indexing: attribute
-        }
-
-        field t type float {
-            indexing: attribute
-        }
-
-        field relevance type float {
-            indexing: attribute
-        }
-
-        field normalizedCS type float {
-            indexing: attribute
-        }
-
-        field laAt type long {
-            indexing: attribute
-        }
-
-        field hsScore type double {
-            indexing: attribute
-        }
-
-    }
-
-    rank-profile defaultRankProfile inherits default {
-
-        constants {
-            maxSignedSixtyFourBitInteger: 9223372036854775807
-        }
-
-        macro log10_1p(x) {
-            expression: log10(x+1)
-        }
-
-        macro textScoreToUse() {
-            expression: if(isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))
-        }
-
-        macro rCountToUse() {
-            expression: if(isNan(attribute(rCount)) == 1, 0, if(attribute(rCount) < 0, 0, attribute(rCount)))
-        }
-
-        macro uniqueRCountToUse() {
-            expression: if(isNan(attribute(uniqueRCount)) == 1, 0, if(attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))
-        }
-
-        macro uvCountToUse() {
-            expression: if(isNan(attribute(uvCount)) == 1, 0, if(attribute(uvCount) < 0, 0, attribute(uvCount)))
-        }
-
-        macro dvCountToUse() {
-            expression: if(isNan(attribute(dvCount)) == 1, 0, if(attribute(dvCount) < 0, 0, attribute(dvCount)))
-        }
-
-        macro aVoteCountToUse() {
-            expression: if(isNan(attribute(aVoteCount)) == 1, 0, if(attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))
-        }
-
-        macro totalPR() {
-            expression: uniqueRCountToUse + query(voteToRRatio) * (uvCountToUse - dvCountToUse) - aVoteCountToUse
-        }
-
-        macro totalvote() {
-            expression: query(reportaweight) * aVoteCountToUse + dvCountToUse + query(rweight) * uniqueRCountToUse + uvCountToUse
-        }
-
-        macro phat() {
-            expression: if (totalvote == 0, 0, ( query(rweight) * uniqueRCountToUse + uvCountToUse) / totalvote)
-        }
-
-        macro nCScoreToUse() {
-            expression: if (totalPR > 0, log10(totalPR), 0)
-        }
-
-        macro hsScoreToUse() {
-            expression: attribute(hsScore)
-        }
-
-        macro tScoreToUse() {
-            expression: if (isNan(attribute(t)) == 1, 0.6, attribute(t))
-        }
-
-        macro relevanceScoreToUse() {
-            expression: if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))
-        }
-
-        macro freshnessToUse() {
-            expression: if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)
-        }
-
-        macro rankedAt() {
-            expression: now
-        }
-
-        macro createdAtToUse() {
-            expression: if(isNan(attribute(createdAt)) == 1, rankedAt, attribute(createdAt))
-        }
-
-        macro laAtToUse() {
-            expression: if(isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))
-        }
-
-        macro markedAsAAtToUse() {
-            expression: if(isNan(attribute(markedAsAAt)) == 1, maxSignedSixtyFourBitInteger, attribute(markedAsAAt))
-        }
-
-        macro tdToUse() {
-            expression: pow(2, 0 - ((rankedAt - createdAtToUse) / query(decay)))
-        }
-
-        macro commentOverallScore() {
-            expression: query(textweight) * textScoreToUse  + query(communityweight) * nCScoreToUse
-        }
-
-        macro pinScore() {
-            expression: if(isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))
-        }
-
-        macro freshnessRank() {
-            expression: nativeRank + freshness(createdAt)
-        }
-
-        first-phase {
-            expression: nativeRank
-        }
-
-    }
-
-    rank-profile neuralNetworkProfile inherits defaultRankProfile {
-        macro nn_input() {
-            expression {
-                concat(log10_1p(aVoteCountToUse),
-                concat(log10_1p(dvCountToUse),
-                concat(log10_1p(uniqueRCountToUse),
-                concat(log10_1p(uvCountToUse),
-                concat(phat,
-                concat(log10_1p(totalvote),
-                concat(hsScoreToUse,
-                concat(tdToUse,
-                tScoreToUse, x), x), x), x), x), x), x), x)
-            }
-        }
-
-        macro get_model_weights(field) {
-            expression: if(query(field) == 0, constant(field), query(field))
-        }
-
-        macro layer_0() {
-            expression: elu(xw_plus_b(nn_input, get_model_weights(W_0), get_model_weights(b_0), x))
-        }
-        macro layer_1() {
-            expression: elu(xw_plus_b(layer_0, get_model_weights(W_1), get_model_weights(b_1), hidden))
-        }
-        macro layer_out() {
-            expression: sum(xw_plus_b(layer_1, get_model_weights(W_out), get_model_weights(b_out), out))
-        }
-        first-phase {
-            expression: freshnessRank
-        }
-        second-phase {
-            expression: layer_out
-            rerank-count: 2000
-        }
-
-    }
-
-    constant W_0 {
-        file: neural-network-201805/W_0.json
-        type: tensor(x[9],hidden[9])
-    }
-    constant b_0 {
-        file: neural-network-201805/b_0.json
-        type: tensor(hidden[9])
-    }
-    constant W_1 {
-        file: neural-network-201805/W_1.json
-        type: tensor(hidden[9],out[9])
-    }
-    constant b_1 {
-        file: neural-network-201805/b_1.json
-        type: tensor(out[9])
-    }
-    constant W_out {
-        file: neural-network-201805/W_out.json
-        type: tensor(out[9])
-    }
-    constant b_out {
-        file: neural-network-201805/b_out.json
-        type: tensor(out[1])
-    }
-
-}
\ No newline at end of file
diff --git a/config-model/src/test/derived/neuralnet/query-profiles/default.xml b/config-model/src/test/derived/neuralnet/query-profiles/default.xml
deleted file mode 100644
index eef1aaa7f53..00000000000
--- a/config-model/src/test/derived/neuralnet/query-profiles/default.xml
+++ /dev/null
@@ -1,2 +0,0 @@
-<query-profile id="default" type="DefaultQueryProfileType">
-</query-profile>
diff --git a/config-model/src/test/derived/neuralnet/query-profiles/types/DefaultQueryProfileType.xml b/config-model/src/test/derived/neuralnet/query-profiles/types/DefaultQueryProfileType.xml
deleted file mode 100644
index e1659479135..00000000000
--- a/config-model/src/test/derived/neuralnet/query-profiles/types/DefaultQueryProfileType.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<query-profile-type id="DefaultQueryProfileType">
-    <field name="ranking.features.query(W_0)" type="tensor(x[9],hidden[9])" />
-    <field name="ranking.features.query(b_0)" type="tensor(hidden[9])" />
-    <field name="ranking.features.query(W_1)" type="tensor(hidden[9],out[9])" />
-    <field name="ranking.features.query(b_1)" type="tensor(out[9])" />
-    <field name="ranking.features.query(W_out)" type="tensor(out[9])" />
-    <field name="ranking.features.query(b_out)" type="tensor(out[1])" />
-</query-profile-type>
diff --git a/config-model/src/test/derived/neuralnet/rank-profiles.cfg b/config-model/src/test/derived/neuralnet/rank-profiles.cfg
deleted file mode 100644
index 4530bff2e20..00000000000
--- a/config-model/src/test/derived/neuralnet/rank-profiles.cfg
+++ /dev/null
@@ -1,198 +0,0 @@
-rankprofile[].name "default"
-rankprofile[].fef.property[].name "vespa.type.query.b_out"
-rankprofile[].fef.property[].value "tensor(out[1])"
-rankprofile[].fef.property[].name "vespa.type.query.W_out"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_0"
-rankprofile[].fef.property[].value "tensor(hidden[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_1"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_1"
-rankprofile[].fef.property[].value "tensor(hidden[9],out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_0"
-rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"
-rankprofile[].name "unranked"
-rankprofile[].fef.property[].name "vespa.rank.firstphase"
-rankprofile[].fef.property[].value "value(0)"
-rankprofile[].fef.property[].name "vespa.hitcollector.heapsize"
-rankprofile[].fef.property[].value "0"
-rankprofile[].fef.property[].name "vespa.hitcollector.arraysize"
-rankprofile[].fef.property[].value "0"
-rankprofile[].fef.property[].name "vespa.dump.ignoredefaultfeatures"
-rankprofile[].fef.property[].value "true"
-rankprofile[].fef.property[].name "vespa.type.query.b_out"
-rankprofile[].fef.property[].value "tensor(out[1])"
-rankprofile[].fef.property[].name "vespa.type.query.W_out"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_0"
-rankprofile[].fef.property[].value "tensor(hidden[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_1"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_1"
-rankprofile[].fef.property[].value "tensor(hidden[9],out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_0"
-rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"
-rankprofile[].name "defaultRankProfile"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p).rankingScript"
-rankprofile[].fef.property[].value "log10(x + 1)"
-rankprofile[].fef.property[].name "rankingExpression(textScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))"
-rankprofile[].fef.property[].name "rankingExpression(rCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(rCount)) == 1, 0, if (attribute(rCount) < 0, 0, attribute(rCount)))"
-rankprofile[].fef.property[].name "rankingExpression(uniqueRCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(uniqueRCount)) == 1, 0, if (attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))"
-rankprofile[].fef.property[].name "rankingExpression(uvCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(uvCount)) == 1, 0, if (attribute(uvCount) < 0, 0, attribute(uvCount)))"
-rankprofile[].fef.property[].name "rankingExpression(dvCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(dvCount)) == 1, 0, if (attribute(dvCount) < 0, 0, attribute(dvCount)))"
-rankprofile[].fef.property[].name "rankingExpression(aVoteCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(aVoteCount)) == 1, 0, if (attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))"
-rankprofile[].fef.property[].name "rankingExpression(totalPR).rankingScript"
-rankprofile[].fef.property[].value "rankingExpression(uniqueRCountToUse) + query(voteToRRatio) * (rankingExpression(uvCountToUse) - rankingExpression(dvCountToUse)) - rankingExpression(aVoteCountToUse)"
-rankprofile[].fef.property[].name "rankingExpression(totalvote).rankingScript"
-rankprofile[].fef.property[].value "query(reportaweight) * rankingExpression(aVoteCountToUse) + rankingExpression(dvCountToUse) + query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)"
-rankprofile[].fef.property[].name "rankingExpression(phat).rankingScript"
-rankprofile[].fef.property[].value "if (rankingExpression(totalvote) == 0, 0, (query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)) / rankingExpression(totalvote))"
-rankprofile[].fef.property[].name "rankingExpression(nCScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (rankingExpression(totalPR) > 0, log10(rankingExpression(totalPR)), 0)"
-rankprofile[].fef.property[].name "rankingExpression(hsScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "attribute(hsScore)"
-rankprofile[].fef.property[].name "rankingExpression(tScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(t)) == 1, 0.6, attribute(t))"
-rankprofile[].fef.property[].name "rankingExpression(relevanceScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))"
-rankprofile[].fef.property[].name "rankingExpression(freshnessToUse).rankingScript"
-rankprofile[].fef.property[].value "if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)"
-rankprofile[].fef.property[].name "rankingExpression(rankedAt).rankingScript"
-rankprofile[].fef.property[].value "now"
-rankprofile[].fef.property[].name "rankingExpression(createdAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(createdAt)) == 1, rankingExpression(rankedAt), attribute(createdAt))"
-rankprofile[].fef.property[].name "rankingExpression(laAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))"
-rankprofile[].fef.property[].name "rankingExpression(markedAsAAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(markedAsAAt)) == 1, 9.223372036854776E18, attribute(markedAsAAt))"
-rankprofile[].fef.property[].name "rankingExpression(tdToUse).rankingScript"
-rankprofile[].fef.property[].value "pow(2,0 - ((rankingExpression(rankedAt) - rankingExpression(createdAtToUse)) / query(decay)))"
-rankprofile[].fef.property[].name "rankingExpression(commentOverallScore).rankingScript"
-rankprofile[].fef.property[].value "query(textweight) * rankingExpression(textScoreToUse) + query(communityweight) * rankingExpression(nCScoreToUse)"
-rankprofile[].fef.property[].name "rankingExpression(pinScore).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))"
-rankprofile[].fef.property[].name "rankingExpression(freshnessRank).rankingScript"
-rankprofile[].fef.property[].value "nativeRank + freshness(createdAt)"
-rankprofile[].fef.property[].name "vespa.rank.firstphase"
-rankprofile[].fef.property[].value "nativeRank"
-rankprofile[].fef.property[].name "vespa.type.query.b_out"
-rankprofile[].fef.property[].value "tensor(out[1])"
-rankprofile[].fef.property[].name "vespa.type.query.W_out"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_0"
-rankprofile[].fef.property[].value "tensor(hidden[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_1"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_1"
-rankprofile[].fef.property[].value "tensor(hidden[9],out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_0"
-rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"
-rankprofile[].name "neuralNetworkProfile"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p).rankingScript"
-rankprofile[].fef.property[].value "log10(x + 1)"
-rankprofile[].fef.property[].name "rankingExpression(textScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))"
-rankprofile[].fef.property[].name "rankingExpression(rCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(rCount)) == 1, 0, if (attribute(rCount) < 0, 0, attribute(rCount)))"
-rankprofile[].fef.property[].name "rankingExpression(uniqueRCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(uniqueRCount)) == 1, 0, if (attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))"
-rankprofile[].fef.property[].name "rankingExpression(uvCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(uvCount)) == 1, 0, if (attribute(uvCount) < 0, 0, attribute(uvCount)))"
-rankprofile[].fef.property[].name "rankingExpression(dvCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(dvCount)) == 1, 0, if (attribute(dvCount) < 0, 0, attribute(dvCount)))"
-rankprofile[].fef.property[].name "rankingExpression(aVoteCountToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(aVoteCount)) == 1, 0, if (attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))"
-rankprofile[].fef.property[].name "rankingExpression(totalPR).rankingScript"
-rankprofile[].fef.property[].value "rankingExpression(uniqueRCountToUse) + query(voteToRRatio) * (rankingExpression(uvCountToUse) - rankingExpression(dvCountToUse)) - rankingExpression(aVoteCountToUse)"
-rankprofile[].fef.property[].name "rankingExpression(totalvote).rankingScript"
-rankprofile[].fef.property[].value "query(reportaweight) * rankingExpression(aVoteCountToUse) + rankingExpression(dvCountToUse) + query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)"
-rankprofile[].fef.property[].name "rankingExpression(phat).rankingScript"
-rankprofile[].fef.property[].value "if (rankingExpression(totalvote) == 0, 0, (query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)) / rankingExpression(totalvote))"
-rankprofile[].fef.property[].name "rankingExpression(nCScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (rankingExpression(totalPR) > 0, log10(rankingExpression(totalPR)), 0)"
-rankprofile[].fef.property[].name "rankingExpression(hsScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "attribute(hsScore)"
-rankprofile[].fef.property[].name "rankingExpression(tScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(t)) == 1, 0.6, attribute(t))"
-rankprofile[].fef.property[].name "rankingExpression(relevanceScoreToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))"
-rankprofile[].fef.property[].name "rankingExpression(freshnessToUse).rankingScript"
-rankprofile[].fef.property[].value "if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)"
-rankprofile[].fef.property[].name "rankingExpression(rankedAt).rankingScript"
-rankprofile[].fef.property[].value "now"
-rankprofile[].fef.property[].name "rankingExpression(createdAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(createdAt)) == 1, rankingExpression(rankedAt), attribute(createdAt))"
-rankprofile[].fef.property[].name "rankingExpression(laAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))"
-rankprofile[].fef.property[].name "rankingExpression(markedAsAAtToUse).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(markedAsAAt)) == 1, 9.223372036854776E18, attribute(markedAsAAt))"
-rankprofile[].fef.property[].name "rankingExpression(tdToUse).rankingScript"
-rankprofile[].fef.property[].value "pow(2,0 - ((rankingExpression(rankedAt) - rankingExpression(createdAtToUse)) / query(decay)))"
-rankprofile[].fef.property[].name "rankingExpression(commentOverallScore).rankingScript"
-rankprofile[].fef.property[].value "query(textweight) * rankingExpression(textScoreToUse) + query(communityweight) * rankingExpression(nCScoreToUse)"
-rankprofile[].fef.property[].name "rankingExpression(pinScore).rankingScript"
-rankprofile[].fef.property[].value "if (isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))"
-rankprofile[].fef.property[].name "rankingExpression(freshnessRank).rankingScript"
-rankprofile[].fef.property[].value "nativeRank + freshness(createdAt)"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p@af9a8c53ba738798).rankingScript"
-rankprofile[].fef.property[].value "log10(rankingExpression(aVoteCountToUse) + 1)"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p@6ad21b437fe95dd9).rankingScript"
-rankprofile[].fef.property[].value "log10(rankingExpression(dvCountToUse) + 1)"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p@c05478688f81fe20).rankingScript"
-rankprofile[].fef.property[].value "log10(rankingExpression(uniqueRCountToUse) + 1)"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p@53f0a2c000e82f4).rankingScript"
-rankprofile[].fef.property[].value "log10(rankingExpression(uvCountToUse) + 1)"
-rankprofile[].fef.property[].name "rankingExpression(log10_1p@d7da61ad34902e89).rankingScript"
-rankprofile[].fef.property[].value "log10(rankingExpression(totalvote) + 1)"
-rankprofile[].fef.property[].name "rankingExpression(nn_input).rankingScript"
-rankprofile[].fef.property[].value "concat(rankingExpression(log10_1p@af9a8c53ba738798), concat(rankingExpression(log10_1p@6ad21b437fe95dd9), concat(rankingExpression(log10_1p@c05478688f81fe20), concat(rankingExpression(log10_1p@53f0a2c000e82f4), concat(rankingExpression(phat), concat(rankingExpression(log10_1p@d7da61ad34902e89), concat(rankingExpression(hsScoreToUse), concat(rankingExpression(tdToUse), rankingExpression(tScoreToUse), x), x), x), x), x), x), x), x)"
-rankprofile[].fef.property[].name "rankingExpression(nn_input).type"
-rankprofile[].fef.property[].value "tensor(x[9])"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights).rankingScript"
-rankprofile[].fef.property[].value "if (query(field) == 0, constant(field), query(field))"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@1f2b4afc2c45fbee).rankingScript"
-rankprofile[].fef.property[].value "if (query(W_0) == 0, constant(W_0), query(W_0))"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@e752cecc7900ff3e).rankingScript"
-rankprofile[].fef.property[].value "if (query(b_0) == 0, constant(b_0), query(b_0))"
-rankprofile[].fef.property[].name "rankingExpression(layer_0).rankingScript"
-rankprofile[].fef.property[].value "elu(join(reduce(join(rankingExpression(nn_input), rankingExpression(get_model_weights@1f2b4afc2c45fbee), f(a,b)(a * b)), sum, x), rankingExpression(get_model_weights@e752cecc7900ff3e), f(a,b)(a + b)))"
-rankprofile[].fef.property[].name "rankingExpression(layer_0).type"
-rankprofile[].fef.property[].value "tensor(hidden[9])"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@eac265fa16b752cf).rankingScript"
-rankprofile[].fef.property[].value "if (query(W_1) == 0, constant(W_1), query(W_1))"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@b953c19adb7d2154).rankingScript"
-rankprofile[].fef.property[].value "if (query(b_1) == 0, constant(b_1), query(b_1))"
-rankprofile[].fef.property[].name "rankingExpression(layer_1).rankingScript"
-rankprofile[].fef.property[].value "elu(join(reduce(join(rankingExpression(layer_0), rankingExpression(get_model_weights@eac265fa16b752cf), f(a,b)(a * b)), sum, hidden), rankingExpression(get_model_weights@b953c19adb7d2154), f(a,b)(a + b)))"
-rankprofile[].fef.property[].name "rankingExpression(layer_1).type"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@418462473aa32b7d).rankingScript"
-rankprofile[].fef.property[].value "if (query(W_out) == 0, constant(W_out), query(W_out))"
-rankprofile[].fef.property[].name "rankingExpression(get_model_weights@23f46853cab72961).rankingScript"
-rankprofile[].fef.property[].value "if (query(b_out) == 0, constant(b_out), query(b_out))"
-rankprofile[].fef.property[].name "rankingExpression(layer_out).rankingScript"
-rankprofile[].fef.property[].value "reduce(join(reduce(join(rankingExpression(layer_1), rankingExpression(get_model_weights@418462473aa32b7d), f(a,b)(a * b)), sum, out), rankingExpression(get_model_weights@23f46853cab72961), f(a,b)(a + b)), sum)"
-rankprofile[].fef.property[].name "vespa.rank.firstphase"
-rankprofile[].fef.property[].value "rankingExpression(freshnessRank)"
-rankprofile[].fef.property[].name "vespa.rank.secondphase"
-rankprofile[].fef.property[].value "rankingExpression(layer_out)"
-rankprofile[].fef.property[].name "vespa.hitcollector.heapsize"
-rankprofile[].fef.property[].value "2000"
-rankprofile[].fef.property[].name "vespa.type.query.b_out"
-rankprofile[].fef.property[].value "tensor(out[1])"
-rankprofile[].fef.property[].name "vespa.type.query.W_out"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_0"
-rankprofile[].fef.property[].value "tensor(hidden[9])"
-rankprofile[].fef.property[].name "vespa.type.query.b_1"
-rankprofile[].fef.property[].value "tensor(out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_1"
-rankprofile[].fef.property[].value "tensor(hidden[9],out[9])"
-rankprofile[].fef.property[].name "vespa.type.query.W_0"
-rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"