Not everything parallelizes. Some features have sequential dependencies. While I could force parallelism in some features with worktrees and then try and merge things, it creates merge conflicts and can lead to confusion which leads to more work and bad merges. I prefer to keep things atomic and incremental to avoid spending too much time fighting a merge conflict in a codebase I barely recognize.
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anchor = self.anchor.unsqueeze(0).repeat(batch_size, 1, 1)
next tuple of R. The selection node repeatedly calls next() on the table scan until it finds a tuple that satisfies a5. It then passes it up to the projection which finally returns it back to you.