def METHOD_NAME(
    self,
    *,  # force keyword-only arguments
    image_rgb: Optional[torch.Tensor],
    camera: CamerasBase,
    fg_probability: Optional[torch.Tensor],
    mask_crop: Optional[torch.Tensor],
    depth_map: Optional[torch.Tensor],
    sequence_name: Optional[List[str]],
    evaluation_mode: EvaluationMode = EvaluationMode.EVALUATION,
    **kwargs,
) -> Dict[str, Any]:
    """
    Args:
        image_rgb: A tensor of shape `(B, 3, H, W)` containing a batch of rgb images;
            the first `min(B, n_train_target_views)` images are considered targets and
            are used to supervise the renders; the rest corresponding to the source
            viewpoints from which features will be extracted.
        camera: An instance of CamerasBase containing a batch of `B` cameras corresponding
            to the viewpoints of target images, from which the rays will be sampled,
            and source images, which will be used for intersecting with target rays.
        fg_probability: A tensor of shape `(B, 1, H, W)` containing a batch of
            foreground masks.
        mask_crop: A binary tensor of shape `(B, 1, H, W)` deonting valid
            regions in the input images (i.e. regions that do not correspond
            to, e.g., zero-padding). When the `RaySampler`'s sampling mode is set to
            "mask_sample", rays  will be sampled in the non zero regions.
        depth_map: A tensor of shape `(B, 1, H, W)` containing a batch of depth maps.
        sequence_name: A list of `B` strings corresponding to the sequence names
            from which images `image_rgb` were extracted. They are used to match
            target frames with relevant source frames.
        evaluation_mode: one of EvaluationMode.TRAINING or
            EvaluationMode.EVALUATION which determines the settings used for
            rendering.
    Returns:
        preds: A dictionary containing all outputs of the forward pass. All models should
            output an instance of `ImplicitronRender` in `preds["implicitron_render"]`.
    """
    raise NotImplementedError()