class_docstr: MultiClassifierDL for Multi-label Text Classification.
  
  MultiClassifierDL Bidirectional GRU with Convolution model we have built
  inside TensorFlow and supports up to 100 classes.
  
  In machine learning, multi-label classification and the strongly related
  problem of multi-output classification are variants of the classification
  problem where multiple labels may be assigned to each instance. Multi-label
  classification is a generalization of multiclass classification, which is
  the single-label problem of categorizing instances into precisely one of
  more than two classes; in the multi-label problem there is no constraint on
  how many of the classes the instance can be assigned to. Formally,
  multi-label classification is the problem of finding a model that maps
  inputs x to binary vectors y (assigning a value of 0 or 1 for each element
  (label) in y).
  
  The input to ``MultiClassifierDL`` are Sentence Embeddings such as the
  state-of-the-art :class:`.UniversalSentenceEncoder`,
  :class:`.BertSentenceEmbeddings`, :class:`.SentenceEmbeddings` or other
  sentence embeddings.
  
  This is the instantiated model of the :class:`.MultiClassifierDLApproach`.
  For training your own model, please see the documentation of that class.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> multiClassifier = MultiClassifierDLModel.pretrained() \
  >>>     .setInputCols(["sentence_embeddings"]) \
  >>>     .setOutputCol("categories")
  
  The default model is ``"multiclassifierdl_use_toxic"``, if no name is
  provided. It uses embeddings from the UniversalSentenceEncoder and
  classifies toxic comments.
  
  The data is based on the
  `Jigsaw Toxic Comment Classification Challenge <https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview>`__.
  For available pretrained models please see the `Models Hub <https://sparknlp.org/models?task=Text+Classification>`__.
  
  For extended examples of usage, see the `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb>`__.
  
  ======================= ======================
  Input Annotation types  Output Annotation type
  ======================= ======================
  ``SENTENCE_EMBEDDINGS`` ``CATEGORY``
  ======================= ======================
  
  Parameters
  ----------
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  threshold
      The minimum threshold for each label to be accepted, by default 0.5
  classes
      Get the tags used to trained this MultiClassifierDLModel
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> useEmbeddings = UniversalSentenceEncoder.pretrained() \
  ...     .setInputCols("document") \
  ...     .setOutputCol("sentence_embeddings")
  >>> multiClassifierDl = MultiClassifierDLModel.pretrained() \
  ...     .setInputCols("sentence_embeddings") \
  ...     .setOutputCol("classifications")
  >>> pipeline = Pipeline() \
  ...     .setStages([
  ...         documentAssembler,
  ...         useEmbeddings,
  ...         multiClassifierDl
  ...     ])
  >>> data = spark.createDataFrame([
  ...     ["This is pretty good stuff!"],
  ...     ["Wtf kind of crap is this"]
  ... ]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.select("text", "classifications.result").show(truncate=False)
  +--------------------------+----------------+
  |text                      |result          |
  +--------------------------+----------------+
  |This is pretty good stuff!|[]              |
  |Wtf kind of crap is this  |[toxic, obscene]|
  +--------------------------+----------------+
  
  See Also
  --------
  ClassifierDLModel : for single-class classification
  SentimentDLModel : for sentiment analysis
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docstr: Sets configProto from tensorflow, serialized into byte array.
  
  Parameters
  ----------
  b : List[int]
      ConfigProto from tensorflow, serialized into byte array
docstr_tok: ["Sets","configProto","from","tensorflow",",","serialized","into","byte","array",".","Parameters","--","--","--","--","--","b",":","List","[","int","]","ConfigProto","from","tensorflow",",","serialized","into","byte","array"]
code: def setConfigProtoBytes(self, b):
      """Sets configProto from tensorflow, serialized into byte array.
  
      Parameters
      ----------
      b : List[int]
          ConfigProto from tensorflow, serialized into byte array
      """
      return self._set(configProtoBytes=b)
  
file_path: spark-nlp/python/sparknlp/annotator/classifier_dl/multi_classifier_dl.py
imports: from sparknlp.annotator.param import EvaluationDLParams, ClassifierEncoder
  from sparknlp.annotator.classifier_dl import ClassifierDLModel
  from sparknlp.common import *
label_desc: Use image node_id 3 for calling the MultiClassifierDLModel obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self