docstr: Returns the component's default config.
docstr_tok: ["Returns","the","component","'s","default","config","."]
code: def get_default_config() -> Dict[Text, Any]:
      """Returns the component's default config."""
      return {
          **DenseFeaturizer.get_default_config(),
          # specifies the language of the subword segmentation model
          "lang": None,
          # specifies the dimension of the subword embeddings
          "dim": None,
          # specifies the vocabulary size of the segmentation model
          "vs": None,
          # if set to True and the given vocabulary size can't be loaded for the given
          # model, the closest size is chosen
          "vs_fallback": True,
      }
  
imports: import numpy
  import logging
  from bpemb import BPEmb
  from typing import Any, Text, Dict, List, Type
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  from rasa.nlu.tokenizers.tokenizer import Tokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import DENSE_FEATURIZABLE_ATTRIBUTES, FEATURIZER_CLASS_ALIAS
  from rasa.shared.nlu.constants import TEXT, TEXT_TOKENS, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
label_desc: Use image node_id 3 for calling the BytePairFeaturizer obj's underlying member method code with example usage: obj.get_default_config() and returns: dict