// initialize underlying tensor memory:
var data = [9]i32{ 1, 2, 3, 4, 5, 6, 7, 8, 9 };

// create a rank 2, 3x3, Rowwise tensor of i32 from data:
var X = zein.Tensor(i32, 2, Rowwise).init(&data, .{ 3, 3 });    

const x = X.getValue(.{0, 2}); // access value 3...

// null will automatically use the GPA allocator:
var factory = zein.TensorFactory(f32).init(null);

// Begin tracking tensor allocations (default is no-tracking):
factory.tracking(.start);

// Stop tracking tensor allocations (does not free tensors):
factory.tracking(.stop);

// Free tracked tensor allocations (no-op if no tensors are tracked):
factory.tracking(.free);

// Deinit will free the allocator and currently tracked tensors:
factory.deinit();

// Assign a new tensor from allocator:
var Y = try factory.allocTensor(2, Rowwise, .{ 10, 10 });

// Assign memory into existing tensor:
var X = Tensor(f32, 2, Rowwise).init(null, .{ 10, 10 });

try factory.allocToTensor(&X); // alloc 100 elements...

// Collapse tensor values using contraction:

try zein.contraction("ijk->ji", &x, &y); // free function - assign to existing memory

var y = factory.contraction("ijk->ji", &x); // factory function - allocate new memory

// Elementary binary functions (add, multiply):

try zein.add(&x, &y, &z); // free function - assign to existing memory

var x = factory.add(&x, &y); // factory function - allocate new memory

// Transpose/permutate tensor views (does not modify underlying data).

var y = try x.permutate("ijk->kji");

// Elementary vectorized reduction functions (sum, product, min, max):
const a = try zein.sum(&x);
const b = try zein.product(&x);
const c = try zein.max(&x);
const d = try zein.min(&x);