class_docstr: This Named Entity recognition annotator is a generic NER model based on
  Neural Networks.
  
  Neural Network architecture is Char CNNs - BiLSTM - CRF that achieves
  state-of-the-art in most datasets.
  
  This is the instantiated model of the :class:`.NerDLApproach`. For training
  your own model, please see the documentation of that class.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> nerModel = NerDLModel.pretrained() \
  ...     .setInputCols(["sentence", "token", "embeddings"]) \
  ...     .setOutputCol("ner")
  
  
  The default model is ``"ner_dl"``, if no name is provided.
  
  For available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Named+Entity+Recognition>`__.
  Additionally, pretrained pipelines are available for this module, see
  `Pipelines <https://sparknlp.org/docs/en/pipelines>`__.
  
  Note that some pretrained models require specific types of embeddings,
  depending on which they were trained on. For example, the default model
  ``"ner_dl"`` requires the WordEmbeddings ``"glove_100d"``.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/model-downloader/Create%20custom%20pipeline%20-%20NerDL.ipynb>`__.
  
  ==================================== ======================
  Input Annotation types               Output Annotation type
  ==================================== ======================
  ``DOCUMENT, TOKEN, WORD_EMBEDDINGS`` ``NAMED_ENTITY``
  ==================================== ======================
  
  Parameters
  ----------
  batchSize
      Size of every batch, by default 8
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  includeConfidence
      Whether to include confidence scores in annotation metadata, by default
      False
  includeAllConfidenceScores
      Whether to include all confidence scores in annotation metadata or just
      the score of the predicted tag, by default False
  classes
      Tags used to trained this NerDLModel
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  
  First extract the prerequisites for the NerDLModel
  
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token")
  >>> embeddings = WordEmbeddingsModel.pretrained() \
  ...     .setInputCols(["sentence", "token"]) \
  ...     .setOutputCol("bert")
  
  Then NER can be extracted
  
  >>> nerTagger = NerDLModel.pretrained() \
  ...     .setInputCols(["sentence", "token", "bert"]) \
  ...     .setOutputCol("ner")
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentence,
  ...     tokenizer,
  ...     embeddings,
  ...     nerTagger
  ... ])
  >>> data = spark.createDataFrame([["U.N. official Ekeus heads for Baghdad."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.select("ner.result").show(truncate=False)
  +------------------------------------+
  |result                              |
  +------------------------------------+
  |[B-ORG, O, O, B-PER, O, O, B-LOC, O]|
  +------------------------------------+
  
  See Also
  --------
  NerCrfModel : for a generic CRF approach
  NerConverter : to further process the results
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)
  
imports: import sys
  from sparknlp.annotator.param import EvaluationDLParams
  from sparknlp.common import *
  from sparknlp.annotator.ner.ner_approach import NerApproach
label_desc: Use image node_id 2 for calling the NerDLModel obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self