[LayerNorm] Support inconsecutive dimensions as axes

[lisa0314]

Using reduceMean and add, mul, div, sqrt to emulation when axes are inconsecutive.

function layerNormalization(builder, input, options) {
// The reduction of the mean and variance values happens over the spatial
// dimensions across all the input features (i.e. all channels) of the input
// tensor.
const reduceOptions = {axes: [1, 2, 3], keepDimensions: true};
const mean = builder.reduceMean(input, reduceOptions);
const variance = builder.reduceMean(
builder.pow(builder.sub(input, mean), builder.constant(input.dataType, 2)),
reduceOptions);

// The scale and bias tensors are of the shape of the input
// specified by the values in the axes parameter (i.e. [1,2,3]).
return builder.add(
builder.mul(
options.scale,
builder.div(
builder.sub(input, mean),
builder.sqrt(builder.add(variance, options.epsilon)))),
options.bias);
}