def __init__(self, num_features, num_channel=64, out_channel=1):
        """Initializes the MaskDecoder with specified parameters."""
        super(MaskDecoder, self).__init__()
        self.dense_block = DilatedDenseNet(depth=4, in_channels=num_channel)  # Dense feature extraction
        self.sub_pixel = SPConvTranspose2d(num_channel, num_channel, (1, 3), 2)  # Sub-pixel convolution for upsampling
        self.conv_1 = nn.Conv2d(num_channel, out_channel, (1, 2))  # Convolution layer to produce mask
        self.norm = nn.InstanceNorm2d(out_channel, affine=True)  # Normalization layer
        self.prelu = nn.PReLU(out_channel)  # Activation function
        self.final_conv = nn.Conv2d(out_channel, out_channel, (1, 1))  # Final convolution layer
        self.prelu_out = nn.PReLU(num_features, init=-0.25)  # Final activation for output mask
Initializes the MaskDecoder with specified parameters.
clearvoice/clearvoice/models/mossformer_gan_se/generator.py