class_docstr: Language generation pipeline using any `ModelWithLMHead`. This pipeline predicts the words that will follow a
  specified text prompt.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> generator = pipeline(model="gpt2")
  >>> generator("I can't believe you did such a ", do_sample=False)
  [{'generated_text': "I can't believe you did such a icky thing to me. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I"}]
  
  >>> # These parameters will return suggestions, and only the newly created text making it easier for prompting suggestions.
  >>> outputs = generator("My tart needs some", num_return_sequences=4, return_full_text=False)
  ```
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial). You can pass text
  generation parameters to this pipeline to control stopping criteria, decoding strategy, and more. Learn more about
  text generation parameters in [Text generation strategies](../generation_strategies) and [Text
  generation](text_generation).
  
  This language generation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"text-generation"`.
  
  The models that this pipeline can use are models that have been trained with an autoregressive language modeling
  objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available models
  on [huggingface.co/models](https://huggingface.co/models?filter=text-generation).
class_docstr_tok: ["Language","generation","pipeline","using","any","`","ModelWithLMHead","`",".","This","pipeline","predicts","the","words","that","will","follow","a","specified","text","prompt",".","Example",":","``","`","python",">",">",">","from","transformers","import","pipeline",">",">",">","generator","=","pipeline","(","model=","''","gpt2","''",")",">",">",">","generator","(","``","I","ca","n't","believe","you","did","such","a","``",",","do_sample=False",")","[","{","'generated_text","'",":","``","I","ca","n't","believe","you","did","such","a","icky","thing","to","me",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","'m","so","sorry",".","I","''","}","]",">",">",">","#","These","parameters","will","return","suggestions",",","and","only","the","newly","created","text","making","it","easier","for","prompting","suggestions",".",">",">",">","outputs","=","generator","(","``","My","tart","needs","some","''",",","num_return_sequences=4",",","return_full_text=False",")","``","`","Learn","more","about","the","basics","of","using","a","pipeline","in","the","[","pipeline","tutorial","]","(","..","\/pipeline_tutorial",")",".","You","can","pass","text","generation","parameters","to","this","pipeline","to","control","stopping","criteria",",","decoding","strategy",",","and","more",".","Learn","more","about","text","generation","parameters","in","[","Text","generation","strategies","]","(","..","\/generation_strategies",")","and","[","Text","generation","]","(","text_generation",")",".","This","language","generation","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","text-generation","''","`",".","The","models","that","this","pipeline","can","use","are","models","that","have","been","trained","with","an","autoregressive","language","modeling","objective",",","which","includes","the","uni-directional","models","in","the","library","(","e.g",".","gpt2",")",".","See","the","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=text-generation",")","."]
docstr: Parse arguments and tokenize
docstr_tok: ["Parse","arguments","and","tokenize"]
code: def _parse_and_tokenize(self, *args, **kwargs):
      """
      Parse arguments and tokenize
      """
      # Parse arguments
      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
          kwargs.update({"add_space_before_punct_symbol": True})
  
      return super()._parse_and_tokenize(*args, **kwargs)
  
file_path: transformers/src/transformers/pipelines/text_generation.py
imports: import enum
  import warnings
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 3 for calling the TextGenerationPipeline obj's underlying member method code with example usage: obj._parse_and_tokenize() and returns: super