Extract CTE definitions from a query’s tokens and return the resulting virtual tables. Each name AS (body) (or name(c1, c2) AS (body)) becomes a Table whose columns come from running infer_columns_from_tokens on the body. RECURSIVE CTEs use the anchor (first) branch via the existing tok_strip_compound, so recursive self-references are not analysed.

Find every derived table in the token stream — FROM (SELECT ...), JOIN (SELECT ...), JOIN LATERAL (SELECT ...), and comma-LATERAL , LATERAL (SELECT ...) — and build a virtual Table for each. Each body is resolved against catalog via infer_columns_from_tokens, so nested CTEs / VALUES / derived tables compose naturally. An explicit AS alias(c1, c2) column list overrides the body’s names.

Find every (VALUES ...) AS alias(c1, c2, ...) in the token stream and build a virtual Table for each. Column types come from the first row’s literals; rows with unsupported expressions are skipped silently (the resolver will surface any downstream issue).

Token-based column inference, exposed so callers (notably the CTE virtual-table builder in query_analyzer) can reuse the SELECT resolver without re-lexing or constructing a synthetic ParsedQuery.

Same as infer_columns_from_tokens, but when the inner query has no FROM clause of its own, fall back to outer_tables so correlated references like SELECT (SELECT books.id) can still be resolved against the enclosing query’s FROM list.

Every entry point also runs VALUES and derived-table discovery over its own token scope and augments the catalog before resolution, so nested subqueries pick up sibling virtual tables without extra work from the caller.