Why follow Flan-T5 template when T5 tokenizer ignores multiple newlines

🀗Transformers
1

The T5 tokenizer ignores the multiple newlines and encode them as single whitespace ‘_’ before next token:

model_name='google/flan-t5-base'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
tokenizer("Hello how are you\n\nI am fine").tokens()

['▁Hello', '▁how', '▁are', '▁you', '▁I', '▁am', '▁fine', '</s>']

If thats the case then why there are multiple newlines in the Flan-T5 tempelates here to follow. For example:

FewShotPattern(
            inputs="Here is a dialogue:\n{dialogue}\n\nWrite a short summary!",
            targets="{summary}",
            inputs_prefix="",
            targets_prefix="summary: ",
            x_y_delimiter="\n\n",
            example_separator="\n\n"),
...

Even the DeepLearning.AI course was suggesting to add ‘\n\n\n’ to separate shot examples:

def make_prompt(example_indices_full, example_index_to_summarize):
    prompt = ''
    for index in example_indices_full:
        dialogue = dataset['test'][index]['dialogue']
        summary = dataset['test'][index]['summary']
        
        # The stop sequence '{summary}\n\n\n' is important for FLAN-T5. Other models may have their own preferred stop sequence.
        prompt += f"""
Dialogue:

{dialogue}

What was going on?
{summary}


"""

but multiple newlines do not change the encoded input then why?