old_contents: import tensorflow as tf
  
  
  # Create some wrappers for simplicity
  def lrelu(x, leak=0.2, name="lrelu"):
       with tf.variable_scope(name):
           f1 = 0.5 * (1 + leak)
           f2 = 0.5 * (1 - leak)
           return f1 * x + f2 * abs(x)
  
  def conv2d(x, W, b, strides=1):
      # Conv2D wrapper, with bias and relu activation
      x = tf.nn.conv2d(x, W, strides=[1, strides, 1, 1], padding='SAME')
      x = tf.nn.bias_add(x, b)
      return lrelu(x)
  
  def upconv2d(x, W, b, strides=1):
      # Conv2D wrapper, with bias and relu activation
      x = tf.nn.conv2d_transpose(x, W, output_shape=(batch_size, sr, 1, 1),
                                 strides=(1, strides, strides, 1), padding='SAME')
      x = tf.nn.bias_add(x, b)
      return lrelu(x)
  
  
  def maxpool2d(x, k=2):
      # MaxPool2D wrapper
      return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                            padding='SAME')
  
  
  # Create model
  def conv_net(x, weights, biases, dropout):
      xs = tf.reshape(x, shape=[-1, sr, 1, 1])
      # Convolution Layer
      conv1 = conv2d(xs, weights['wc1'], biases['bc1'])
      conv1 = maxpool2d(conv1, k=2)
      print('conv1: ', conv1)
      conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
      conv2 = maxpool2d(conv2, k=2)
      print('conv2: ', conv2)
      #conv3 = SubpixelConv2d(x, scale=2, n_out_channel=2, name='subpixel_conv2d2')
      conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
  
      print('conv3: ', conv3)
      #conv4 = PS(conv3, 2)
      conv4 = tf.depth_to_space(conv3, 2)
      print('conv4: ', conv4)
      #out=
      out = tf.reshape(conv3, [-1, weights['out'].get_shape().as_list()[0]])
      print(out)
      #out = tf.add(tf.matmul(out, weights['out']), biases['out'])
      #print(out)
      return out , conv1, conv2, conv3
  
new_contents: import tensorflow as tf
  
  
  # Create some wrappers for simplicity
  def lrelu(x, leak=0.2, name="lrelu"):
       with tf.variable_scope(name):
           f1 = 0.5 * (1 + leak)
           f2 = 0.5 * (1 - leak)
           return f1 * x + f2 * abs(x)
  
  def conv2d(x, W, b, strides=1):
      # Conv2D wrapper, with bias and relu activation
      x = tf.nn.conv2d(x, W, strides=[1, strides, 1, 1], padding='SAME')
      x = tf.nn.bias_add(x, b)
      return lrelu(x)
  
  def upconv2d(x, W, b, strides=1):
      # Conv2D wrapper, with bias and relu activation
      x = tf.nn.conv2d_transpose(x, W, output_shape=(batch_size, sr, 1, 1),
                                 strides=(1, strides, strides, 1), padding='SAME')
      x = tf.nn.bias_add(x, b)
      return lrelu(x)
  
  
  def maxpool2d(x, k=2):
      # MaxPool2D wrapper
      return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                            padding='SAME')
  
  
  
subject: remove unused function