def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs):
        r"""
        Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
        Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's features into a Feature Pyramid
        Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`.

        Args:
            feature_size (`int`, *optional*, defaults to 256):
                The feature size (channel dimension) of the FPN feature maps.
            mask_feature_size (`int`, *optional*, defaults to 256):
                The features (channels) of the target masks size \\(C_{\epsilon}\\) in the paper.
        """
        super().__init__()

        self.fpn = MaskFormerFPNModel(*args, feature_size=feature_size, **kwargs)
        self.mask_projection = nn.Conv2d(feature_size, mask_feature_size, kernel_size=3, padding=1)

        Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
        Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's features into a Feature Pyramid
        Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`.

        Args:
            feature_size (`int`, *optional*, defaults to 256):
                The feature size (channel dimension) of the FPN feature maps.
            mask_feature_size (`int`, *optional*, defaults to 256):
                The features (channels) of the target masks size \\(C_{\epsilon}\\) in the paper.
        
dpkd/transformers/src/transformers/models/maskformer/modeling_maskformer.py