code: def _apply_sigmoid_gating(x):
      """Apply the sigmoid gating in Figure 2 of [2]."""
      activation_tensor, gate_tensor = tf.split(x, 2, axis=-1)
      sigmoid_gate = tf.sigmoid(gate_tensor)
      return tf_keras.layers.multiply(
          [sigmoid_gate, activation_tensor], dtype=x.dtype
      )
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import quantized_distribution
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import weight_norm
label_desc: Use image node_id 3 for calling a global function with example usage: _apply_sigmoid_gating(x) and returns: tf_keras