class_docstr: GPT2: the OpenAI Text-To-Text Transformer
  
  GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million
  web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within
  some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of
  many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained
  on more than 10X the amount of data.
  
  GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of
  unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In
  addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books)
  without needing to use these domain-specific training datasets. On language tasks like question answering, reading
  comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no
  task-specific training data. While scores on these downstream tasks are far from state-of-the-art, they suggest
  that the tasks can benefit from unsupervised techniques, given sufficient (unlabeled) data and compute.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> gpt2 = GPT2Transformer.pretrained() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("generation")
  
  
  The default model is ``"gpt2"``, if no name is provided. For available
  pretrained models please see the `Models Hub
  <https://sparknlp.org/models?q=gpt2>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT``           ``DOCUMENT``
  ====================== ======================
  
  Parameters
  ----------
  task
      Transformer's task, e.g. ``summarize:`` , by default ""
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  minOutputLength
      Minimum length of the sequence to be generated, by default 0
  maxOutputLength
      Maximum length of output text, by default 20
  doSample
      Whether or not to use sampling; use greedy decoding otherwise, by default False
  temperature
      The value used to module the next token probabilities, by default 1.0
  topK
      The number of highest probability vocabulary tokens to keep for
      top-k-filtering, by default 50
  topP
      Top cumulative probability for vocabulary tokens, by default 1.0
  
      If set to float < 1, only the most probable tokens with probabilities
      that add up to ``topP`` or higher are kept for generation.
  repetitionPenalty
      The parameter for repetition penalty, 1.0 means no penalty. , by default
      1.0
  noRepeatNgramSize
      If set to int > 0, all ngrams of that size can only occur once, by
      default 0
  ignoreTokenIds
      A list of token ids which are ignored in the decoder's output, by
      default []
  
  Notes
  -----
  This is a very computationally expensive module especially on larger
  sequence. The use of an accelerator such as GPU is recommended.
  
  References
  ----------
  - `Language Models are Unsupervised Multitask Learners
    <https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__
  - https://github.com/openai/gpt-2
  
  **Paper Abstract:**
  
  *Natural language processing tasks, such as question answering, machine translation, reading comprehension, and
  summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that
  language models begin to learn these tasks without any explicit supervision when trained on a new dataset
  of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by
  the language model reach F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline
  systems without using the 127,000+ training examples. The capacity of the language model is essential to the
  success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks.
  Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8
  tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model
  reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path
  towards building language processing systems which learn to perform tasks from their naturally occurring
  demonstrations.*
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("documents")
  >>> gpt2 = GPT2Transformer.pretrained("gpt2") \
  ...     .setInputCols(["documents"]) \
  ...     .setMaxOutputLength(50) \
  ...     .setOutputCol("generation")
  >>> pipeline = Pipeline().setStages([documentAssembler, gpt2])
  >>> data = spark.createDataFrame([["My name is Leonardo."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.select("summaries.generation").show(truncate=False)
  +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  |result                                                                                                                                                                                              |
  +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  |[My name is Leonardo. I am a man of letters. I have been a man for many years. I was born in the year 1776. I came to the United States in 1776, and I have lived in the United Kingdom since 1776.]|
  -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
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docstr: Sets maximum length of output text.
  
  Parameters
  ----------
  value : int
      Maximum length of output text
docstr_tok: ["Sets","maximum","length","of","output","text",".","Parameters","--","--","--","--","--","value",":","int","Maximum","length","of","output","text"]
code: def setMaxOutputLength(self, value):
      """Sets maximum length of output text.
  
      Parameters
      ----------
      value : int
          Maximum length of output text
      """
      return self._set(maxOutputLength=value)
  
file_path: spark-nlp/python/sparknlp/annotator/seq2seq/gpt2_transformer.py
imports: from sparknlp.common import *
label_desc: Use image node_id 5 for calling the GPT2Transformer obj's underlying member method code with example usage: obj.setMaxOutputLength(value) and returns: self