> ## Documentation Index
> Fetch the complete documentation index at: https://luminair.gizatech.xyz/llms.txt
> Use this file to discover all available pages before exploring further.

# GraphTensor

> The high-level interface for writing ML code, checked at compile time.

**GraphTensors** are a fundamental part of LuminAIR, serving as references to nodes within the computational graph.
They provide a high-level interface for defining machine learning models and operations, ensuring that all computations are checked at compile time.

# Creating a GraphTensor

To get started, you can create a `GraphTensor` by initializing a graph and adding tensor nodes:

```rust theme={null}
let mut cx = Graph::new(); // Initialize a new computational graph
let a: GraphTensor<R1<3>> = cx.tensor(); // Create a new tensor node in the graph
```

Here:

* `Graph::new()` initializes a new computational graph.
* `GraphTensor<R1<3>>` represents a tensor node with metadata about its shape (in this case, rank-1 with size 3).

# Performing Operations with GraphTensors

Once you have created `GraphTensors`, you can perform various linear algebra operations, similar to libraries like [PyTorch](https://pytorch.org/):

```rust theme={null}
let b = a.exp().sqrt(); // Apply exponentiation and square root operations
let c = b + a; // Add tensors b and a
```

Operations like `b + a` do not consume the original tensors (`a` and `b`).
Both remain available for reuse in subsequent operations. This is made possible because `GraphTensor` is a lightweight tracking structure that implements the `Copy` trait.

This design allows operations on `GraphTensor` to construct a computational graph without immediate execution, so no values are assigned to the `GraphTensor` at this stage.
Actual computations are postponed until `cx.gen_trace()` is invoked, which enables the compiler to optimize the execution process effectively.

For more details on how to build, run, and prove graphs, check out this [guide](/quick-start).