class_docstr: Language generation pipeline using any `ModelWithLMHead`. This pipeline predicts the words that will follow a
  specified text prompt.
  
  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: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/text_generation.py
imports: import enum
  import warnings
  from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
  from ..utils import add_end_docstrings, is_tf_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