src/transformers/pipelines/text2text_generation.py/TranslationPipeline/__call__
class TranslationPipeline:    def __call__(self, *args, **kwargs):
        r"""
        Translate the text(s) given as inputs.

        Args:
            args (:obj:`str` or :obj:`List[str]`):
                Texts to be translated.
            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to include the decoded texts in the outputs.
            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to clean up the potential extra spaces in the text output.
            src_lang (:obj:`str`, `optional`):
                The language of the input. Might be required for multilingual models. Will not have any effect for
                single pair translation models
            tgt_lang (:obj:`str`, `optional`):
                The language of the desired output. Might be required for multilingual models. Will not have any effect
                for single pair translation models
            generate_kwargs:
                Additional keyword arguments to pass along to the generate method of the model (see the generate method
                corresponding to your framework `here <./model.html#generative-models>`__).

        Return:
            A list or a list of list of :obj:`dict`: Each result comes as a dictionary with the following keys:

            - **translation_text** (:obj:`str`, present when ``return_text=True``) -- The translation.
            - **translation_token_ids** (:obj:`torch.Tensor` or :obj:`tf.Tensor`, present when ``return_tensors=True``)
              -- The token ids of the translation.
        """
        return super().__call__(*args, **kwargs)