[Question]Retrieval with CategoricalCrossentropy really minimizing the affinity between the query and negative candidates?

In tfrs.tasks.retireval document, retrieval task is explained like
The main argument are pairs of query and candidate embeddings: the first row of query_embeddings denotes a query for which the candidate from the first row of candidate embeddings was selected by the user.The task will try to maximize the affinity of these query, candidate pairs while minimizing the affinity between the query and candidates belonging to other queries in the batch.
The default loss function of tfrs.tasks.Retrieval is tf.keras.losses.CategoricalCrossentropy. But in CategoricalCrossentropy, the loss of label 0 candidate become 0.

So,

• if I use CategoricalCrossentropy for retrieval loss function, the label 1 candidate will affect loss value and embeddings, but the label 0 candidate will not.Is that right?
• if I set num_hard_negatives argument with CategoricalCrossentropy loss, the number of negative(label 0)candidates will decrease, but the loss value will not change.Is that right?

Start with the linked tfrs.tasks.Retrieval documentation and the behavior of tf.keras.losses.CategoricalCrossentropy. Trace how Retrieval.call handles candidate labels and num_hard_negatives, then clarify in the documentation whether label-0 candidates affect the loss and embeddings and whether hard-negative selection changes the loss.