parameters: (pc)
return_statement: return pc, centroid, furthest_distance
docstring: pc [N, P, 3]
docstring_summary: pc [N, P, 3]
docstring_tokens: pc [ N P 3 ]
function: def normalize_point_cloud(pc):
      """
      pc [N, P, 3]
      """
      centroid = tf.reduce_mean(pc, axis=1, keepdims=True)
      pc = pc - centroid
      furthest_distance = tf.reduce_max(
          tf.sqrt(tf.reduce_sum(pc ** 2, axis=-1, keepdims=True)), axis=1, keepdims=True)
      pc = pc / furthest_distance
      return pc, centroid, furthest_distance
function_tokens: def normalize_point_cloud ( pc ) : centroid = tf . reduce_mean ( pc , axis = 1 , keepdims = True ) pc = pc - centroid furthest_distance = tf . reduce_max ( tf . sqrt ( tf . reduce_sum ( pc ** 2 , axis = - 1 , keepdims = True ) ) , axis = 1 , keepdims = True ) pc = pc / furthest_distance return pc , centroid , furthest_distance