Initialize the variables used for the `BatchNorm`s (if any).

def _create_batch_norm_variables(self, dtype):
    """Initialize the variables used for the `BatchNorm`s (if any)."""
    # The paper recommends a value of 0.1 for good gradient flow through the
    # tanh nonlinearity (although doesn't say whether this is for all gammas,
    # or just some).
    gamma_initializer = tf.constant_initializer(0.1)

    if self._use_batch_norm_h:
      self._gamma_h = tf.get_variable(
          self.GAMMA_H,
          shape=[4 * self._hidden_size],
          dtype=dtype,
          initializer=self._initializers.get(self.GAMMA_H, gamma_initializer),
          partitioner=self._partitioners.get(self.GAMMA_H),
          regularizer=self._regularizers.get(self.GAMMA_H))
    if self._use_batch_norm_x:
      self._gamma_x = tf.get_variable(
          self.GAMMA_X,
          shape=[4 * self._hidden_size],
          dtype=dtype,
          initializer=self._initializers.get(self.GAMMA_X, gamma_initializer),
          partitioner=self._partitioners.get(self.GAMMA_X),
          regularizer=self._regularizers.get(self.GAMMA_X))
    if self._use_batch_norm_c:
      self._gamma_c = tf.get_variable(
          self.GAMMA_C,
          shape=[self._hidden_size],
          dtype=dtype,
          initializer=self._initializers.get(self.GAMMA_C, gamma_initializer),
          partitioner=self._partitioners.get(self.GAMMA_C),
          regularizer=self._regularizers.get(self.GAMMA_C))
      self._beta_c = tf.get_variable(
          self.BETA_C,
          shape=[self._hidden_size],
          dtype=dtype,
          initializer=self._initializers.get(self.BETA_C),
          partitioner=self._partitioners.get(self.BETA_C),
          regularizer=self._regularizers.get(self.BETA_C))