class_docstr: Longformer is a transformer model for long documents. The Longformer
  model was presented in `Longformer: The Long-Document Transformer` by Iz
  Beltagy, Matthew E. Peters, Arman Cohan. longformer-base-4096 is a BERT-like
  model started from the RoBERTa checkpoint and pretrained for MLM on long
  documents. It supports sequences of length up to 4,096.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> embeddings = LongformerEmbeddings.pretrained() \
  ...     .setInputCols(["document", "token"]) \
  ...     .setOutputCol("embeddings")
  
  
  The default model is ``"longformer_base_4096"``, if no name is provided. For
  available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Embeddings>`__.
  
  To see which models are compatible and how to import them see
  `Import Transformers into Spark NLP 🚀
  <https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT, TOKEN``    ``WORD_EMBEDDINGS``
  ====================== ======================
  
  Parameters
  ----------
  batchSize
      Size of every batch, by default 8
  dimension
      Number of embedding dimensions, by default 768
  caseSensitive
      Whether to ignore case in tokens for embeddings matching, by default
      True
  maxSentenceLength
      Max sentence length to process, by default 1024
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  References
  ----------
  `Longformer: The Long-Document Transformer
  <https://arxiv.org/pdf/2004.05150.pdf>`__
  
  
  **Paper Abstract:**
  
  *Transformer-based models are unable to process long sequences due to their
  self-attention operation, which scales quadratically with the sequence
  length. To address this limitation, we introduce the Longformer with an
  attention mechanism that scales linearly with sequence length, making it
  easy to process documents of thousands of tokens or longer. Longformer's
  attention mechanism is a drop-in replacement for the standard self-attention
  and combines a local windowed attention with a task motivated global
  attention. Following prior work on long-sequence transformers, we evaluate
  Longformer on character-level language modeling and achieve state-of-the-art
  results on text8 and enwik8. In contrast to most prior work, we also
  pretrain Longformer and finetune it on a variety of downstream tasks. Our
  pretrained Longformer consistently outperforms RoBERTa on long document
  tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We
  finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant
  for supporting long document generative sequence-to-sequence tasks, and
  demonstrate its effectiveness on the arXiv summarization dataset.*
  
  The original code can be found at `Longformer: The Long-Document Transformer
  <https://github.com/allenai/longformer>`__.
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("token")
  >>> embeddings = LongformerEmbeddings.pretrained() \
  ...     .setInputCols(["document", "token"]) \
  ...     .setOutputCol("embeddings") \
  ...     .setCaseSensitive(True)
  >>> embeddingsFinisher = EmbeddingsFinisher() \
  >>>     .setInputCols(["embeddings"]) \
  ...     .setOutputCols("finished_embeddings") \
  ...     .setOutputAsVector(True) \
  ...     .setCleanAnnotations(False)
  >>> pipeline = Pipeline() \
  ...     .setStages([
  ...         documentAssembler,
  ...         tokenizer,
  ...         embeddings,
  ...         embeddingsFinisher
  ...     ])
  >>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
  +--------------------------------------------------------------------------------+
  |                                                                          result|
  +--------------------------------------------------------------------------------+
  |[0.18792399764060974,-0.14591649174690247,0.20547787845134735,0.1468472778797...|
  |[0.22845706343650818,0.18073144555091858,0.09725798666477203,-0.0417917296290...|
  |[0.07037967443466187,-0.14801117777824402,-0.03603338822722435,-0.17893412709...|
  |[-0.08734266459941864,0.2486150562763214,-0.009067727252840996,-0.24408400058...|
  |[0.22409197688102722,-0.4312366545200348,0.1401449590921402,0.356410235166549...|
  +--------------------------------------------------------------------------------+
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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/embeddings/longformer_embeddings.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the LongformerEmbeddings obj's underlying member method code with example usage: obj.setConfigProtoBytes(b) and returns: self