code: def test_attention_mask(
      actual_sequence_length: int,
      max_input_sequence_length: int,
      zero_start_index: int,
      create_language_model_featurizer: Callable[
          [Dict[Text, Any]], LanguageModelFeaturizer
      ],
      monkeypatch: MonkeyPatch,
  ):
      monkeypatch.setattr(
          LanguageModelFeaturizer,
          "_load_model_instance",
          lambda _: None,
      )
      component = create_language_model_featurizer(
          {"model_name": "bert"}
      )
  
      attention_mask = component._compute_attention_mask(
          [actual_sequence_length], max_input_sequence_length
      )
      mask_ones = attention_mask[0][:zero_start_index]
      mask_zeros = attention_mask[0][zero_start_index:]
  
      assert np.all(mask_ones == 1)
      assert np.all(mask_zeros == 0)
  
imports: import os
  from typing import Text, List, Dict, Tuple, Any, Callable
  import numpy
  import pytest
  import logging
  from _pytest.monkeypatch import MonkeyPatch
  from _pytest.logging import LogCaptureFixture
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.storage.resource import Resource
  from rasa.nlu.constants import TOKENS_NAMES, NUMBER_OF_SUB_TOKENS
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.featurizers.dense_featurizer.lm_featurizer import LanguageModelFeaturizer
  from rasa.shared.nlu.constants import TEXT, INTENT
  from rasa.nlu.tokenizers.tokenizer import Token
label_desc: Use image node_id 11 for calling a global function with example usage: test_attention_mask(actual_sequence_length, max_input_sequence_length, zero_start_index, create_language_model_featurizer, monkeypatch) without return types