segformer model implementation != original arch design

In the segformer paper, the diagram looks like this

But in this repo, the code is written as below. How come it has encoder name attribute, there's no CNN feature extraction separately in the original design plan?

    @supports_config_loading
    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_depth: int = 5,
        encoder_weights: Optional[str] = "imagenet",
        decoder_segmentation_channels: int = 256,
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[Union[str, Callable]] = None,
        upsampling: int = 4,
        aux_params: Optional[dict] = None,
        **kwargs: dict[str, Any],
    ):
        super().__init__()

        self.encoder = get_encoder(
            encoder_name,
            in_channels=in_channels,
            depth=encoder_depth,
            weights=encoder_weights,
            **kwargs,
        )

        self.decoder = SegformerDecoder(
            encoder_channels=self.encoder.out_channels,
            encoder_depth=encoder_depth,
            segmentation_channels=decoder_segmentation_channels,
        )

        self.segmentation_head = SegmentationHead(
            in_channels=decoder_segmentation_channels,
            out_channels=classes,
            activation=activation,
            kernel_size=1,
            upsampling=upsampling,
        )

        if aux_params is not None:
            self.classification_head = ClassificationHead(
                in_channels=self.encoder.out_channels[-1], **aux_params
            )
        else:
            self.classification_head = None

        self.name = "segformer-{}".format(encoder_name)
        self.initialize()

Start with segmentation_models_pytorch/decoders/segformer/model.py and compare its constructor, encoder setup, and decoder wiring with the SegFormer paper diagram linked in the issue. Determine whether the encoder_name and get_encoder path are intentional; document the discrepancy or identify the implementation change needed, with relevant tests updated if behavior changes.