How wide transformations are influenced by shuffle partition config

How does wide transformations actually work based on shuffle partitions configuration?

If I have following program:

spark.conf.set("spark.sql.shuffle.partitions", "5")
val df = spark
    .read
    .option("inferSchema", "true")
    .option("header", "true")
    .csv("...\input.csv")
df.sort("sal").take(200)

Does it mean sort would output 5 new partitions(as configured), and then spark takes 200 records from those 5 partitions?