original_docstring: 
      Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.
      :param vgg_layer3_out: TF Tensor for VGG Layer 3 output
      :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
      :param vgg_layer7_out: TF Tensor for VGG Layer 7 output
      :param num_classes: Number of classes to classify
      :return: The Tensor for the last layer of output
      
docstring: Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.
docstring_tokens: Create the layers for a fully convolutional network . Build skip - layers using the vgg layers .
code: def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes):
      """
      Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.
      :param vgg_layer3_out: TF Tensor for VGG Layer 3 output
      :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
      :param vgg_layer7_out: TF Tensor for VGG Layer 7 output
      :param num_classes: Number of classes to classify
      :return: The Tensor for the last layer of output
      """
      # TODO: Implement function
      conv7_1x1 = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, padding='same',
                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
      conv4_1x1 = tf.layers.conv2d(vgg_layer4_out, num_classes, 1, padding='same',
                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
      conv3_1x1 = tf.layers.conv2d(vgg_layer3_out, num_classes, 1, padding='same',
                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
  
      output = tf.layers.conv2d_transpose(conv7_1x1, num_classes, 4, strides=(2, 2), padding='same', kernel_regularizer=tf.contrib.layers.l2_regularizer(
          1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
      skip_layer4 = tf.add(output, conv4_1x1)
  
      output = tf.layers.conv2d_transpose(skip_layer4, num_classes, 4, strides=(2, 2), padding='same', kernel_regularizer=tf.contrib.layers.l2_regularizer(
          1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
      skip_layer3 = tf.add(output, conv3_1x1)
  
      output = tf.layers.conv2d_transpose(skip_layer3, num_classes, 16, strides=(8, 8), padding='same', kernel_regularizer=tf.contrib.layers.l2_regularizer(
          1e-3), kernel_initializer=tf.random_normal_initializer(stddev=0.02))
  
      return output
code_tokens: def layers ( vgg_layer3_out , vgg_layer4_out , vgg_layer7_out , num_classes ) : conv7_1x1 = tf . layers . conv2d ( vgg_layer7_out , num_classes , 1 , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) conv4_1x1 = tf . layers . conv2d ( vgg_layer4_out , num_classes , 1 , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) conv3_1x1 = tf . layers . conv2d ( vgg_layer3_out , num_classes , 1 , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) output = tf . layers . conv2d_transpose ( conv7_1x1 , num_classes , 4 , strides = ( 2 , 2 ) , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) skip_layer4 = tf . add ( output , conv4_1x1 ) output = tf . layers . conv2d_transpose ( skip_layer4 , num_classes , 4 , strides = ( 2 , 2 ) , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) skip_layer3 = tf . add ( output , conv3_1x1 ) output = tf . layers . conv2d_transpose ( skip_layer3 , num_classes , 16 , strides = ( 8 , 8 ) , padding = 'same' , kernel_regularizer = tf . contrib . layers . l2_regularizer ( 1e-3 ) , kernel_initializer = tf . random_normal_initializer ( stddev = 0.02 ) ) return output
short_docstring: Create the layers for a fully convolutional network.
short_docstring_tokens: Create the layers for a fully convolutional network .
comment: """
      Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.
      :param vgg_layer3_out: TF Tensor for VGG Layer 3 output
      :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
      :param vgg_layer7_out: TF Tensor for VGG Layer 7 output
      :param num_classes: Number of classes to classify
      :return: The Tensor for the last layer of output
      """$SEP$# TODO: Implement function