src/transformers/models/deit/modeling_deit.py/DeiTForMaskedImageModeling/__init__
class DeiTForMaskedImageModeling:    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.deit = DeiTModel(config, add_pooling_layer=False, use_mask_token=True)

        self.decoder = nn.Sequential(
            nn.Conv2d(
                in_channels=config.hidden_size,
                out_channels=config.encoder_stride**2 * config.num_channels,
                kernel_size=1,
            ),
            nn.PixelShuffle(config.encoder_stride),
        )

        # Initialize weights and apply final processing
        self.post_init()