HuggingFace Summarization: effect of specifying both `do_sample` and `num_beams`

I am using a HuggingFace summarization pipeline to generate summaries using a fine-tuned model. The summarizer object is initialised as follows:

from transformers import pipeline

summarizer = pipeline(
    "summarization", 
    model=model, 
    tokenizer=tokenizer, 
    num_beams=5, 
    do_sample=True, 
    no_repeat_ngram_size=3,
    max_length=1024,
    device=0,
    batch_size=8
)

According to the documentation, setting num_beams=5 means that the top 5 choices are retained when a new token in the sequence is generated based on a language model, and the model moves forward discarding all other possibilities, and repeating this after every new token is generated. However, this option seems to be apparently incompatible with do_sample=True which seems to activate a behaviour where new tokens are picked based on some random strategy (which doesn't have to be uniformly random of course, but I don't know the details of this process). Could anyone explain clearly how num_beams=5 and do_sample=True would work together (no error is raised so I assume this is a valid summarizer configuration)?