import tensorflow as tf

                """Layers used during upsampling"""
                with tf.variable_scope(name):
                    if reuse:
                        tf.get_variable_scope().reuse_variables()
                    else:
                        assert tf.get_variable_scope().reuse is False


                    u = tf.contrib.layers.conv2d_transpose(layer_input,filters,f_size,stride=stride,padding=padding)
                    
                    if dropout_rate:
                        u = tf.contrib.layers.dropout(u,keep_prob=dropout_rate)
                    u = tf.contrib.layers.batch_norm(u)
                    u = tf.nn.relu(u)
                   # u = tf.contrib.keras.layers.concatenate([skip_input,u])
                    return u 
            
            
            
            # Downsampling
            dwn1 = common_conv2d(image,self.gf,stride=2,norm=False,name='dwn1')  #  64x64 -> 32x32
	    #print('dwn1',np.shape(dwn1))
