def __init__(self, n_layers, n_hidden, spatial_width, n_colors, n_scales, filter_size=3):
        super(MultiLayerConvolution, self).__init__()

        self.children = []
        num_channels = n_colors
        for ii in xrange(n_layers):
            conv_layer = MultiScaleConvolution(num_channels, n_hidden, spatial_width, n_scales, filter_size, name="layer%d_"%ii)
            self.children.append(conv_layer)
            num_channels = n_hidden
A brick implementing a multi-layer, multi-scale convolutional network.
dataset/ETHPy150Open Sohl-Dickstein/Diffusion-Probabilistic-Models/regression.py/MultiLayerConvolution.__init__