code: def skip_on_CI_with_bert(
      model_name: Text, model_weights: Text
  ) -> bool:
      """Checks whether to skip this configuration on CI.
  
      Only applies when skip_model_load=False
      """
      # First check if CI
      return (
          bool(os.environ.get("CI"))
          and model_name == "bert"
          and (not model_weights or model_weights == "rasa/LaBSE")
      )
  
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 3 for calling a global function with example usage: skip_on_CI_with_bert(model_name, model_weights) and returns: unknown