class_docstr: Summarize news articles and other documents.
  
  This summarizing pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"summarization"`.
  
  The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
  currently, '*bart-large-cnn*', '*t5-small*', '*t5-base*', '*t5-large*', '*t5-3b*', '*t5-11b*'. See the up-to-date
  list of available models on [huggingface.co/models](https://huggingface.co/models?filter=summarization). For a list
  of available parameters, see the [following
  documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
  
  Usage:
  
  ```python
  # use bart in pytorch
  summarizer = pipeline("summarization")
  summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
  
  # use t5 in tf
  summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
  summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
  ```
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docstr: Checks whether there might be something wrong with given input with regard to the model.
docstr_tok: ["Checks","whether","there","might","be","something","wrong","with","given","input","with","regard","to","the","model","."]
code: def check_inputs(
      self, input_length: int, min_length: int, max_length: int
  ) -> bool:
      """
      Checks whether there might be something wrong with given input with regard to the model.
      """
      if max_length < min_length:
          logger.warning(
              f"Your min_length={min_length} must be inferior than your max_length={max_length}."
          )
  
      if input_length < max_length:
          logger.warning(
              f"Your max_length is set to {max_length}, but you input_length is only {input_length}. You might "
              f"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"
          )
  
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 2 for calling the SummarizationPipeline obj's underlying member method code with example usage: obj.check_inputs(input_length, min_length, max_length) without return types