`tf.mixed_precision. experimental.LossScale` in the constructor, and no
        longer has a `LossScale` associated with it. Instead, `Model.compile`
        will automatically wrap the optimizer with a `LossScaleOptimizer` using
        dynamic loss scaling if `Policy.name` is "mixed_float16".
    *   `tf.keras.mixed_precision.LossScaleOptimizer`'s constructor takes in

```

`tf.fill` differs from `tf.constant` in a few ways:

*   `tf.fill` only supports scalar contents, whereas `tf.constant` supports
    Tensor values.
*   `tf.fill` creates an Op in the computation graph that constructs the actual
    Tensor value at runtime. This is in contrast to `tf.constant` which embeds
    the entire Tensor into the graph with a `Const` node.
*   Because `tf.fill` evaluates at graph runtime, it supports dynamic shapes