def __call__(self, images: torch.Tensor) -> torch.Tensor:
        """
        Args:
            images (torch.Tensor): tensor of size [N, 3, H, W] that contains
                BGR data (typically in uint8)
        Returns:
            images (torch.Tensor): tensor of size [N, 3, H1, W1] where
                H1 and W1 are chosen to respect the specified min and max sizes
                and preserve the original aspect ratio, the data channels
                follow BGR order and the data type is `torch.float32`
        """
        # resize with min size
        images = images.float()
        min_size = min(images.shape[-2:])
        max_size = max(images.shape[-2:])
        scale = min(self.min_size / min_size, self.max_size / max_size)
        images = torch.nn.functional.interpolate(
            images,
            scale_factor=scale,
            mode="bilinear",
            align_corners=False,
        )
        return images

        Args:
            images (torch.Tensor): tensor of size [N, 3, H, W] that contains
                BGR data (typically in uint8)
        Returns:
            images (torch.Tensor): tensor of size [N, 3, H1, W1] where
                H1 and W1 are chosen to respect the specified min and max sizes
                and preserve the original aspect ratio, the data channels
                follow BGR order and the data type is `torch.float32`
        
EVA-02/det/projects/DensePose/densepose/data/transform/image.py