code: def transform(self, value):
      
      with tf.name_scope(self._name + '/transform'):
        no_batch_dim = value.shape.ndims == self._mean.shape.ndims
        if no_batch_dim:
          # Add a batch dimension if necessary.
          value = value[None, ...]
        if self._center:
          value -= self._mean[None, ...]
        if self._scale:
          # We cannot scale before seeing at least two samples.
          value /= tf.cond(
              self._count > 1, lambda: self._std() + 1e-8,
              lambda: tf.ones_like(self._var_sum))[None]
        if self._clip:
          value = tf.clip_by_value(value, -self._clip, self._clip)
        # Remove batch dimension if necessary.
        if no_batch_dim:
          value = value[0]
        return tf.check_numerics(value, 'value')
docs: Normalize a single or batch tensor.
  
      Applies the activated transformations in the constructor using current
      estimates of mean and variance.
  
      Args:
        value: Batch or single value tensor.
  
      Returns:
        Normalized batch or single value tensor.