cargo build --release -p codegeex4-cli # Cpu
cargo build --release -p codegeex4-cli --features cuda # if cuda is avalibe
./target/release/codegeex4-cli --sample-len 500

avx: false, neon: false, simd128: false, f16c: false
temp: 0.95 repeat-penalty: 1.10 repeat-last-n: 64
cache path /root/autodl-tmp
Prompt: [please write a FFT in rust]
Using Seed 11511762269791786684
DType is BF16
transofrmer layers create
模型加载完毕 4
starting the inference loop

 开始生成
samplelen 500

500 tokens generated (34.60 token/s)
Result:

Sure, I can help you with that. Here's an example of a Fast Fourier Transform (FFT) implementation in Rust:

```rust
use num_complex::Complex;

fn fft(input: &[Complex<f64> > ] ) -> Vec<Complex<f64> > > {
    let n = input.len();
  
    if n == 1 {
	  return vec![input[0]]];
    }
  
    let mut even = vec![];
    let mut odd = vec![];
  
    for i in 0..n {

	      if i % 2 == 0 {
	      even.push(input[i]);
	  } else {
	      odd.push(input[i]);
	  }
    }
  
    let even_fft = fft(&even);
    let odd_fft = fft(&odd);
  
    let mut output = vec![];
  
    for k in 0..n/2 {
	  let t = Complex::new(0.0, -2.0 * std::f64::consts::PI * (k as f64) / (n as f64))) ).exp();
      
	  output.push(even_fft[k] + odd_fft[k] * t]);
	  output.push(even_fft[k] - odd_fft[k] * t]);
    }
  
    return output;
}
```

This implementation uses the Cooley-Tukey algorithm to perform the FFT. The function takes an array of complex numbers and returns an array of complex numbers which is the result of the FFT.