code: def test_top_p():
      # Test small values
      args_small = GENSettings(top_p=0.01, min_length=20, max_length=20)
      output_small = happy_gen.generate_text(
          "Artificial intelligence is ", args=args_small
      )
      tokens_small = happy_gen.tokenizer.encode(
          output_small.text, return_tensors="pt"
      )
      length_small = len(tokens_small[0])
      assert length_small == 20
  
      # Test large values
      args_large = GENSettings(top_p=1, min_length=20, max_length=20)
      output_large = happy_gen.generate_text(
          "Artificial intelligence is ", args=args_large
      )
      tokens_large = happy_gen.tokenizer.encode(
          output_large.text, return_tensors="pt"
      )
      length_large = len(tokens_large[0])
      assert length_large == 20
  
imports: import torch
  from happytransformer import HappyGeneration, GENSettings, GENTrainArgs, GENEvalArgs
  import pytest
  from tests.run_save_load import run_save_load
  from tests import happy_gen
label_desc: Use image node_id 4 for calling a global function with example usage: test_top_p() without return types