this unification step succeeds as well.
Unification of the LHS and RHS of the equation is now finished.
Type inference succeeds because there is only one type equation,
no unification step failed, and the map is fully populated.
</p>

<p>
Unification uses a combination of <i>exact</i> and <i>loose</i>
unification depending on whether two types have to be

    replaced with by writing regular Python functions. Using the `tf.function`
    decorator, such functions can be turned into graphs which can be executed
    remotely, serialized, and optimized for performance.
*   Unification of `tf.train.Optimizers` and `tf.keras.Optimizers`. Use
    `tf.keras.Optimizers` for TF2.0. `compute_gradients` is removed as public
    API, use `GradientTape` to compute gradients.

 * remain in their original namespace. This reduces the final Gradle distribution
 * size and makes us more conscious of which parts of a library we really need.
 *
 * WARNING: if you decide to do the minification by hand, make sure that you cover all paths of loading classes:
 * reflection, dynamic loading, etc. and understand how the library works internally.
 * These changes might break things in subtle ways otherwise.
 */

position so that the document size can be calculated excluding\n  // the size of the popper element itself\n  const transformProp = getSupportedPropertyName('transform');\n  const popperStyles = data.instance.popper.style; // assignment to help minification\n  const { top, left, [transformProp]: transform } = popperStyles;\n  popperStyles.top = '';\n  popperStyles.left = '';\n  popperStyles[transformProp] = '';\n\n  const boundaries = getBoundaries(\n    data.instance.popper,\n    data.instance.reference,\n...