function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (beta == null) beta = 1;
    if (dtype == null) dtype = "float32";
    if (dtype !== "float32" && dtype !== "int32") throw new Error(`Unsupported data type ${dtype}`);
    const rgamma = new (0, _randUtil.RandGamma)(alpha, beta, dtype, seed);
    const res = (0, _buffer.buffer)(shape, dtype);
    for(let i = 0; i < res.values.length; i++)res.values[i] = rgamma.nextValue();
    return res.toTensor();
}
Creates a `tf.Tensor` with values sampled from a gamma distribution.

```js
tf.randomGamma([2, 2], 1).print();
```

@param shape An array of integers defining the output tensor shape.
@param alpha The shape parameter of the gamma distribution.
@param beta The inverse scale parameter of the gamma distribution. Defaults
    to 1.
@param dtype The data type of the output. Defaults to float32.
@param seed The seed for the random number generator.

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