code: def _kl_normal_normal(a, b, name=None):
      """Calculate the batched KL divergence KL(a || b) with a and b Normal.
  
      Args:
        a: instance of a Normal distribution object.
        b: instance of a Normal distribution object.
        name: Name to use for created operations.
          Default value: `None` (i.e., `'kl_normal_normal'`).
  
      Returns:
        kl_div: Batchwise KL(a || b)
      """
      with tf.name_scope(name or "kl_normal_normal"):
          b_scale = tf.convert_to_tensor(
              b.scale
          )  # We'll read it thrice.
          diff_log_scale = tf.math.log(a.scale) - tf.math.log(b_scale)
          return (
              0.5
              * tf.math.squared_difference(
                  a.loc / b_scale, b.loc / b_scale
              )
              + 0.5 * tf.math.expm1(2.0 * diff_log_scale)
              - diff_log_scale
          )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 for calling a global function with example usage: _kl_normal_normal(a, b, name) and returns: unknown