parameters: (x)
return_statement: return tf.nn.max_pool(
          x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
docstring: max_pool_2x2 downsamples a feature map by 2X.
docstring_summary: max_pool_2x2 downsamples a feature map by 2X.
docstring_tokens: max_pool_2x2 downsamples a feature map by 2X .
function: def max_pool_2x2(x):
      """max_pool_2x2 downsamples a feature map by 2X."""
      return tf.nn.max_pool(
          x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
function_tokens: def max_pool_2x2 ( x ) : return tf . nn . max_pool ( x , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' )