Add functionality to split RawImage into channels; Add functionality to return a vertical slice of a Tensor

[BritishWerewolf]

Some models, like U2Net, require images to be processed with all the reds, greens and blue pixels grouped together.

As a result, this PR provides the ability to do something like this.

// Get our image, then split into individual channels.
const image = await RawImage.fromURL("https://picsum.photos/600/400");

// ... snip ...
// Preprocess the image by resizing and padding to a square of 320x320
// See these PRs for functionality to do this: #971, #972

// Image pixel values are an array of [R, G, B, R, G, B,…]
// Use this to separate out each individual channel.
const [R, G, B] = image.toChannels();
// Convert from an array of Uint8Array into regular array, then flatten.
// We now have an array of [R, R, …, G, G, …, B, B, …]
const image_data = [R, G, B].map(d => Array.from(d)).flat();

// Create the tensor for this image
const tensor = new Tensor('float32', image_data, [1, image.channels, image.width, image.height]);
const model = await AutoModel.from_pretrained('u2netp');
const outputs = await model({ "input": tensor });

---

In addition to the toChannels method, I created another for Tensor's too.

With .slice we could return specific rows, this PR adds the ability to return vertical slices of a Tensor.
For some examples, check out the tests I've added.