function divNoNan_(a, b) {
    // TODO: Make this into its own kernel.
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "div");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "div");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const divResult = (0, _div.div)($a, $b);
    const zeros = (0, _zerosLike.zerosLike)(divResult);
    const bEqualsZero = (0, _equal.equal)($b, zeros);
    return (0, _where.where)(bEqualsZero, zeros, divResult);
}
Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
if denominator is 0.


```js
const a = tf.tensor1d([1, 4, 9, 16]);
const b = tf.tensor1d([1, 2, 3, 4]);
const c = tf.tensor1d([0, 0, 0, 0]);

a.divNoNan(b).print();  // or tf.divNoNan(a, b)
a.divNoNan(c).print();  // or tf.divNoNan(a, c)
```

```js
// Broadcast div a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(2);
const c = tf.scalar(0);

a.divNoNan(b).print();  // or tf.divNoNan(a, b)
a.divNoNan(c).print();  // or tf.divNoNan(a, c)
```

@param a The first tensor as the numerator.
@param b The second tensor as the denominator. Must have the same dtype as
`a`.

@doc {heading: 'Operations', subheading: 'Arithmetic'}