def masked_conv_coupling(input_, mask_in, dim, name, use_batch_norm=True, train=True, weight_norm=True, reverse=False, residual_blocks=5, bottleneck=False, use_aff=True, use_width=1.0, use_height=1.0, mask_channel=0.0, skip=True):
 if use_aff:
  return masked_conv_aff_coupling(input_=input_, mask_in=mask_in, dim=dim, name=name, use_batch_norm=use_batch_norm, train=train, weight_norm=weight_norm, reverse=reverse, residual_blocks=residual_blocks, bottleneck=bottleneck, use_width=use_width, use_height=use_height, mask_channel=mask_channel, skip=skip)
 else:
  return masked_conv_add_coupling(input_=input_, mask_in=mask_in, dim=dim, name=name, use_batch_norm=use_batch_norm, train=train, weight_norm=weight_norm, reverse=reverse, residual_blocks=residual_blocks, bottleneck=bottleneck, use_width=use_width, use_height=use_height, mask_channel=mask_channel, skip=skip)

def masked_conv_coupling ( input_ , mask_in , dim , name , use_batch_norm = True , train = True , weight_norm = True , reverse = False , residual_blocks = 5 , bottleneck = False , use_aff = True , use_width = 1.0 , use_height = 1.0 , mask_channel = 0.0 , skip = True ) : if use_aff : return masked_conv_aff_coupling ( input_ = input_ , mask_in = mask_in , dim = dim , name = name , use_batch_norm = use_batch_norm , train = train , weight_norm = weight_norm , reverse = reverse , residual_blocks = residual_blocks , bottleneck = bottleneck , use_width = use_width , use_height = use_height , mask_channel = mask_channel , skip = skip ) else : return masked_conv_add_coupling ( input_ = input_ , mask_in = mask_in , dim = dim , name = name , use_batch_norm = use_batch_norm , train = train , weight_norm = weight_norm , reverse = reverse , residual_blocks = residual_blocks , bottleneck = bottleneck , use_width = use_width , use_height = use_height , mask_channel = mask_channel , skip = skip )
coupling with masked convolution .