def forward(
        self,
        images: torch.Tensor,
        data_samples: Optional[list] = None,
        mode: str = 'predict',
        **kwargs,
    ):
        """The unified entry for a forward process in both training and test.
        The method accepts the following modes:

        - "predict": Forward and return a list of data samples contain the
          predict results.

        Args:
            images (torch.Tensor): the preprocessed image tensor of shape
                ``(N, C, H, W)``.
            data_samples (List[DataSample], optional): The annotation data
                of every samples. Defaults to None.
            mode (str): Return what kind of value. Defaults to 'predict'.
        """
        if mode == 'predict':
            return self.predict(images, data_samples, **kwargs)
        else:
            raise RuntimeError(f'Invalid mode "{mode}".')
The unified entry for a forward process in both training and test.
        The method accepts the following modes:

        - "predict": Forward and return a list of data samples contain the
          predict results.

        Args:
            images (torch.Tensor): the preprocessed image tensor of shape
                ``(N, C, H, W)``.
            data_samples (List[DataSample], optional): The annotation data
                of every samples. Defaults to None.
            mode (str): Return what kind of value. Defaults to 'predict'.
        
mmpretrain/models/multimodal/clip/clip.py