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 (`str` or `List[str]`):
                Texts to be translated.
            return_tensors (`bool`, *optional*, defaults to `False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            return_text (`bool`, *optional*, defaults to `True`):
                Whether or not to include the decoded texts in the outputs.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the potential extra spaces in the text output.
            src_lang (`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 (`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#generative-models)).

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

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