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//! # `wasi-nn`
//!
//! ### Introduction
//!
//! This project provides high-level wasi-nn bindings for Rust. The basic idea: write your machine
//! learning application in a high-level language using these bindings, compile it to WebAssembly,
//! and run it in a WebAssembly runtime that supports the [wasi-nn
//! proposal](https://github.com/WebAssembly/wasi-nn/).
//!
//! ### Usage
//!
//! ```rust
//! use wasi_nn::{GraphBuilder, GraphEncoding, ExecutionTarget, TensorType};
//!
//! fn test(model_path: &'static str) -> Result<(), wasi_nn::Error> {
//! // Prepare input and output buffer; the input and output buffer can be any sized type, such
//! // as u8, f32, etc.
//! let input = vec![0f32; 224 * 224 * 3];
//! let input_dim = vec![1, 224, 224, 3];
//! let mut output_buffer = vec![0f32; 1001];
//!
//! // Build a tflite graph from a file and set an input tensor.
//! let graph = GraphBuilder::new(GraphEncoding::TensorflowLite, ExecutionTarget::CPU).build_from_files([model_path])?;
//! let mut ctx = graph.init_execution_context()?;
//! ctx.set_input(0, TensorType::F32, &input_dim, &input)?;
//!
//! // Do the inference.
//! ctx.compute()?;
//!
//! // Copy output to abuffer.
//! let output_bytes = ctx.get_output(0, &mut output_buffer)?;
//! assert_eq!(output_bytes, output_buffer.len() * std::mem::size_of::<f32>());
//! Ok(())
//! }
//! ```
//!
//! ### Note
//!
//! This crate is experimental and will change to adapt the upstream [wasi-nn
//! proposal](https://github.com/WebAssembly/wasi-nn/).
//!
//! Now version is based on git commit ```f47f35c00c946cb0e3229f11f288bda9d3d12cff```
//!
pub use Error;
pub use ;
pub use TensorType;