code: def inverse_lin_decay(max_step, min_value=0.01, step=None):
    
    if step is None:
      step = tf.train.get_global_step()
    if step is None:
      return 1.0
    step = to_float(step)
    progress = tf.minimum(step / float(max_step), 1.0)
    return progress * (1.0 - min_value) + min_value
docs: Inverse-decay linearly from 0.01 to 1.0 reached at max_step.