All the groundbreaking papers in deep learning in the last decade had code published. So if you're aiming for thousands of citations, you need code.
I am in this field and I would say less than 10% of the top papers have code published by the author, and those are most of the time another 0.1% improvement in imagenet. All the libraries that you generally use are likely to be recreated by others in this field. Lot of most interesting work's code never come out like alphazero/muzero, GPT-3 etc.
I personally look at any paper without code with great suspicion. The reviewers certainly did not try to reproduce your results, and I have no guarantee that a paper without code has enough information for me to reproduce.
I always go for the papers with code provided.
Personally, it is a pet peeve I have about my field. But there is no incentive for a new researcher to publish code as it decreases barriers to entry. As much as it's nice to say that researching in academia is about progressing science, as a researcher, you are your own startup trying to make it (i.e., get tenure).