@classmethod
def Params(cls):
    p = super(BaseConv2DLayerWithPadding, cls).Params()
    p.Define('filter_shape', (0, 0, 0, 0), 'Filter shape. Must be a sequence of length 4. Elements are in the order of height (time), width (frequency), in_channel, out_channel. For causal convolution, filter_shape[0] is the actual number of trained weights in the time dimension of the kernel.')
    p.Define('filter_stride', (1, 1), 'Filter stride to use. Must be a pair of ints. The first int specifies the stride on the time dimension. The second int specifies the stride on the frequency dimension.')
    p.Define('dilation_rate', (1, 1), 'If > 1, dilation rate for atrous convolution. Must be a pair of ints. The first int specifies the dilation rate on the time dimension. The second int specifies the dilation rate on the frequency dimension. If any value of dilation_rate is > 1, then all values of strides must be 1.')
    p.Define('weight_norm', False, 'If true, apply weight normalization to weights as proposed by Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')
    return p
@classmethod
def Params(cls):
    p = super(BaseConv2DLayerWithPadding, cls).Params()
    p.Define('filter_shape', (0, 0, 0, 0), 'Filter shape. Must be a sequence of length 4. Elements are in the order of height (time), width (frequency), in_channel, out_channel. For causal convolution, filter_shape[0] is the actual number of trained weights in the time dimension of the kernel.')
    p.Define('filter_stride', (1, 1), 'Filter stride to use. Must be a pair of ints. The first int specifies the stride on the time dimension. The second int specifies the stride on the frequency dimension.')
    p.Define('dilation_rate', (1, 1), 'If > 1, dilation rate for atrous convolution. Must be a pair of ints. The first int specifies the dilation rate on the time dimension. The second int specifies the dilation rate on the frequency dimension. If any value of dilation_rate is > 1, then all values of strides must be 1.')
    p.Define('weight_norm', False, 'If true, apply weight normalization to weights as proposed by Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')
    return p