code: def _create_batch_norm_variables(self, dtype):
      
      # 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))
docs: Initialize the variables used for the `BatchNorm`s (if any).