class_docstr: Pipeline for text to text generation using seq2seq models.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> generator = pipeline(model="mrm8488/t5-base-finetuned-question-generation-ap")
  >>> generator(
  ...     "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google"
  ... )
  [{'generated_text': 'question: Who created the RuPERTa-base?'}]
  ```
  
  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 Text2TextGenerationPipeline pipeline can currently be loaded from [`pipeline`] using the following task
  identifier: `"text2text-generation"`.
  
  The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
  up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=text2text-generation). For a list of available
  parameters, see the [following
  documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate)
  
  Usage:
  
  ```python
  text2text_generator = pipeline("text2text-generation")
  text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")
  ```
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code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      self.check_model_type(
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
          if self.framework == "tf"
          else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
      )
  
imports: import enum
  import warnings
  from ..tokenization_utils import TruncationStrategy
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 1 to create a new Text2TextGenerationPipeline object from inherited base classes: Pipeline with example: obj = Text2TextGenerationPipeline()