dataset/generator/keras sequence.
    *   Update TF 2.0 `keras.backend.name_scope` to use TF 2.0 `name_scope`.
    *   Add v2 module aliases for losses, metrics, initializers and optimizers:
        `tf.losses = tf.keras.losses` & `tf.metrics = tf.keras.metrics` &
        `tf.initializers = tf.keras.initializers` & `tf.optimizers =
        tf.keras.optimizers`.

    
    From: [@mikedanese](https://github.com/mikedanese):
    Instead of aliasing. Aliases are annoying in a number of ways. This is
    specifically bugging me now because they make the action graph harder to
    analyze programmatically. By using aliases here, we would need to handle
    potentially aliased go_binary targets and dereference to the effective
    target.
  

overwritten. // //go:norace func (out *Nat) Mod(x *Nat, m *Modulus) *Nat { out.resetFor(m) // Working our way from the most significant to the least significant limb, // we can insert each limb at the least significant position, shifting all // previous limbs left by _W. This way each limb will get shifted by the // correct number of bits. We can insert at least N - 1 limbs without // overflowing m. After that, we need to reduce every time we shift. i := len(x.limbs) - 1 // For the first N - 1 limbs...

2018..2023    ; valid                  ;      ; NV8    # 1.1  LEFT SINGLE QUOTATION MARK..TRIANGULAR BULLET
2024..2026    ; disallowed                             # 1.1  ONE DOT LEADER..HORIZONTAL ELLIPSIS
2027          ; valid                  ;      ; NV8    # 1.1  HYPHENATION POINT
2028..202E    ; disallowed                             # 1.1  LINE SEPARATOR..RIGHT-TO-LEFT OVERRIDE

overwritten. // //go:norace func (out *Nat) Mod(x *Nat, m *Modulus) *Nat { out.resetFor(m) // Working our way from the most significant to the least significant limb, // we can insert each limb at the least significant position, shifting all // previous limbs left by _W. This way each limb will get shifted by the // correct number of bits. We can insert at least N - 1 limbs without // overflowing m. After that, we need to reduce every time we shift. i := len(x.limbs) - 1 // For the first N - 1 limbs...

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀