code: def dice(
      _x, axis=-1, epsilon=0.000000001, name="", data_type=tf.float32
  ):
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          alphas = tf.get_variable(
              "alpha" + name,
              _x.get_shape()[-1],
              initializer=tf.constant_initializer(0.0),
              dtype=data_type,
          )
          input_shape = list(_x.get_shape())
  
          reduction_axes = list(range(len(input_shape)))
          del reduction_axes[axis]
          broadcast_shape = [1] * len(input_shape)
          broadcast_shape[axis] = input_shape[axis]
  
      # case: train mode (uses stats of the current batch)
      mean = tf.reduce_mean(_x, axis=reduction_axes)
      brodcast_mean = tf.reshape(mean, broadcast_shape)
      std = tf.reduce_mean(
          tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes
      )
      std = tf.sqrt(std)
      brodcast_std = tf.reshape(std, broadcast_shape)
      x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon)
      # x_normed = tf.layers.batch_normalization(_x, center=False, scale=False)
      x_p = tf.sigmoid(x_normed)
  
      return alphas * (1.0 - x_p) * _x + x_p * _x
  
imports: import tensorflow
  from tensorflow.python.ops.rnn_cell import *
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.util import nest
  from tensorflow.python.ops import nn_ops
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 9 for calling a global function with example usage: dice(_x, axis, epsilon, name, data_type) and returns: unknown