code: def _composite_activation(x, biased=True):
      x = tf.atan(x)
      if biased:
          # Biased Coefficients computed by:
          #   def rms(x):
          #     return np.sqrt((x*x).mean())
          #   a = np.arctan(np.random.normal(0.0, 1.0, 10**6))
          #   print(rms(a), rms(a*a))
          coeffs = (0.67, 0.6)
          means = (0.0, 0.0)
      else:
          # Unbiased Coefficients computed by:
          #   a = np.arctan(np.random.normal(0.0, 1.0, 10**6))
          #   aa = a*a
          #   print(a.std(), aa.mean(), aa.std())
          coeffs = (0.67, 0.396)
          means = (0.0, 0.45)
      composite = [
          (x - means[0]) / coeffs[0],
          (x * x - means[1]) / coeffs[1],
      ]
      return tf.concat(composite, -1)
  
imports: import numpy
  import tensorflow
  from tensorflow.contrib import slim
label_desc: Use image node_id 1 for calling a global function with example usage: _composite_activation(x, biased) and returns: tf