def get_conv_block(in_channel, out_channel, gan_type='sn_gan', normalization='instance', activation='leakyrelu', **kwargs):
    block = []
    block.append(get_conv_layer(in_channel, out_channel, gan_type=gan_type, **kwargs))
    if (normalization == 'instance'):
        block.append(nn.InstanceNorm2d(out_channel))
    if (activation == 'leakyrelu'):
        block.append(nn.LeakyReLU())
    return nn.Sequential(*block)
def get_conv_block(in_channel, out_channel, gan_type='sn_gan', normalization='instance', activation='leakyrelu', **kwargs):
    block = []
    block.append(get_conv_layer(in_channel, out_channel, gan_type=gan_type, **kwargs))
    if (normalization == 'instance'):
        block.append(nn.InstanceNorm2d(out_channel))
    if (activation == 'leakyrelu'):
        block.append(nn.LeakyReLU())
    return nn.Sequential(*block)