e_pretrained`], e.g., `./my_model_directory/`.
                    - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case,
                      `decoder_from_pt` should be set to `True`.

            model_args (remaining positional arguments, *optional*):
                All remaning positional arguments will be passed to the underlying model's `__init__` method.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`).

                - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
                - To update the parent model configuration, do not use a prefix for each configuration parameter.

                Behaves differently depending on whether a `config` is provided or automatically loaded.

        Example:

        ```python
        >>> from transformers import TFVisionEncoderDecoderModel

        >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
        >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
        ...     "google/vit-base-patch16-224-in21k", "bert-base-uncased"
        ... )
        >>> # saving model after fine-tuning
        >>> model.save_pretrained("./vit-bert")
        >>> # load fine-tuned model
        >>> model = TFVisionEncoderDecoderModel.from_pretrained("./vit-bert")
        ```c