parameters: (graph, layers, skips)
return_statement: return t
docstring: Adds the output from the specified skip connections.
  
         Computes the input to the next layer by summing together the outputs
         from the specified skip connections with the output from the previous
         layer. Downsamples outputs with appropriately sized average pooling
         layers to ensure that all tensors have the same size before summing.
  
         Args:
          graph: The TASO graph.
          layers: A list of the output tensors of each layer.
          skips: A list where element i is 1 if the current layer
                 has a skip connection to layer i and 0 otherwise.
  
         Returns:
          The sum of all skip connections and the output of the previous layer.
docstring_summary: Adds the output from the specified skip connections.
docstring_tokens: Adds the output from the specified skip connections .
function: def add_skips(graph, layers, skips):
      """Adds the output from the specified skip connections.
  
         Computes the input to the next layer by summing together the outputs
         from the specified skip connections with the output from the previous
         layer. Downsamples outputs with appropriately sized average pooling
         layers to ensure that all tensors have the same size before summing.
  
         Args:
          graph: The TASO graph.
          layers: A list of the output tensors of each layer.
          skips: A list where element i is 1 if the current layer
                 has a skip connection to layer i and 0 otherwise.
  
         Returns:
          The sum of all skip connections and the output of the previous layer.
      """
      t = layers[-1]
      sizes = \
          set([get_dims(layers[i]) for i in range(len(skips)) if skips[i] == 1])
      sizes.add(get_dims(t))
      min_size = min(sizes)
      for i in range(len(skips)):
          if skips[i] == 1:
              size = get_dims(layers[i])
              if size > min_size:
                  kernel_size = size[-1] - 2 * (min_size[-1] - 1)
                  t = graph.add(
                          graph.avgpool2d(input=layers[i],
                                          kernels=[kernel_size, kernel_size],
                                          strides=[2, 2],
                                          padding="VALID"),
                          t)
              else:
                  t = graph.add(layers[i], t)
      return t
function_tokens: def add_skips ( graph , layers , skips ) : t = layers [ - 1 ] sizes = set ( [ get_dims ( layers [ i ] ) for i in range ( len ( skips ) ) if skips [ i ] == 1 ] ) sizes . add ( get_dims ( t ) ) min_size = min ( sizes ) for i in range ( len ( skips ) ) : if skips [ i ] == 1 : size = get_dims ( layers [ i ] ) if size > min_size : kernel_size = size [ - 1 ] - 2 * ( min_size [ - 1 ] - 1 ) t = graph . add ( graph . avgpool2d ( input = layers [ i ] , kernels = [ kernel_size , kernel_size ] , strides = [ 2 , 2 ] , padding = "VALID" ) , t ) else : t = graph . add ( layers [ i ] , t ) return t