function randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (dtype != null && dtype === "bool") throw new Error(`Unsupported data type ${dtype}`);
    const randGauss = new (0, _randUtil.MPRandGauss)(mean, stdDev, dtype, false, seed);
    const res = (0, _buffer.buffer)(shape, dtype);
    for(let i = 0; i < res.values.length; i++)res.values[i] = randGauss.nextValue();
    return res.toTensor();
}
Creates a `tf.Tensor` with values sampled from a normal distribution.

```js
tf.randomNormal([2, 2]).print();
```

@param shape An array of integers defining the output tensor shape.
@param mean The mean of the normal distribution.
@param stdDev The standard deviation of the normal distribution.
@param dtype The data type of the output.
@param seed The seed for the random number generator.

@doc {heading: 'Tensors', subheading: 'Random'}