old_contents: import tensorflow as tf
  from template import Template
  
  
  class Linear(Template):
  
      def __init__(self, prev_dim=None, this_dim=None, W=None, b=None):
          """
          DESCRIPTION:
              This is a fully connected layer
          PARAM:
              prev_dim(int): dimension of previous layer
              this_dim(int): dimension of this layer
              name(string): name of the layer
              W(tensor variable): Weight of 2D tensor matrix
              b(tensor variable): bias of 2D tensor matrix
              params(list): a list of params in layer that can be updated
          """
  
          self.prev_dim = prev_dim
          self.this_dim = this_dim
  
          self.W = W
          if self.W is None:
              self.W = tf.Variable(tf.random_normal([self.prev_dim, self.this_dim], stddev=0.1), name='W')
  
          self.b = b
          if self.b is None:
              self.b = tf.Variable(tf.random_normal([self.this_dim], stddev=0.1), name='b')
  
      def _train_fprop(self, state_below):
          return tf.matmul(state_below, self.W) + self.b
  
      @property
      def _variables(self):
          return [self.W, self.b]
  
new_contents: import tensorflow as tf
  from template import Template
  
  
  class Linear(Template):
  
      def __init__(self, prev_dim=None, this_dim=None, W=None, b=None):
          """
          DESCRIPTION:
              This is a fully connected layer
          PARAM:
              prev_dim(int): dimension of previous layer
              this_dim(int): dimension of this layer
              W(tensor variable): Weight of 2D tensor matrix
              b(tensor variable): bias of 2D tensor matrix
          """
  
          self.prev_dim = prev_dim
          self.this_dim = this_dim
  
          self.W = W
          if self.W is None:
              self.W = tf.Variable(tf.random_normal([self.prev_dim, self.this_dim], stddev=0.1), name='W')
  
          self.b = b
          if self.b is None:
              self.b = tf.Variable(tf.random_normal([self.this_dim], stddev=0.1), name='b')
  
      def _train_fprop(self, state_below):
          return tf.matmul(state_below, self.W) + self.b
  
      @property
      def _variables(self):
          return [self.W, self.b]
  
subject: remove text