function truncatedNormal_(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, true, 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 truncated normal
distribution.

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

The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2
standard deviations from the mean are dropped and re-picked.

@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 tensor.
@param seed The seed for the random number generator.

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