@@ -177,8 +177,8 @@ The result would be as follows:
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``` text
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Canonical(QR) = for<T, L> {
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certainty: Proven,
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- var_values: [Vec<?0>, '?1, ?2 ]
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- region_constraints: [?2 : '?1],
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+ var_values: [Vec<?0>, '?1, ?0 ]
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+ region_constraints: [?0 : '?1],
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value: (),
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}
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```
@@ -213,8 +213,8 @@ and now we got back a canonical response:
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``` text
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for<T, L> {
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certainty: Proven,
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- var_values: [Vec<?0>, '?1, ?2 ]
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- region_constraints: [?2 : '?1],
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+ var_values: [Vec<?0>, '?1, ?0 ]
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+ region_constraints: [?0 : '?1],
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value: (),
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}
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```
@@ -250,7 +250,7 @@ for later verification.
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than eagerly instantiating all of the canonical values in the result
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with variables, we instead walk the vector of values, looking for
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cases where the value is just a canonical variable. In our example,
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- ` values[2] ` is ` ?C ` , so that means we can deduce that `?C := ?B and
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+ ` values[2] ` is ` ?C ` , so that means we can deduce that ` ?C := ?B ` and
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` '?D := 'static ` . This gives us a partial set of values. Anything for
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which we do not find a value, we create an inference variable.)
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