You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: temporal.rs
    path of file: ./repos/point-process-rust/pylib/src
    
    the code of the file until where you have to start completion: //! Wrappers for the `pointprocesses::temporal` module.
use pointprocesses::temporal::*;
use traits::TemporalProcess;
use pyo3::prelude::*;
use pyo3::types::PyList;
use std::thread;
use numpy::{PyArray1,ToPyArray};
use ndarray::prelude::*;



#[pymodule]
/// Time-dependent point processes.
fn temporal(_py: Python, module: &PyModule) -> PyResult<()> {
    
    #[pyfn(module, "poisson_process")]
    /// Simulate a homogeneous, constant-intensity Poisson process.
    /// 
    /// Args:
    ///     tmax (float): upper time bound.
    ///     lambda (float): base intensity.
    /// 
    /// Returns:
    ///     Process timestamps.
    fn poisson_process_py(py: Python, tmax: f64, lambda: f64) -> Py<PyArray1<f64>> {
        let arr = poisson_process(tmax, lambda);
        arr.to_pyarray(py).to_owned()
    }

    #[pyfn(module, "variable_poisson")]
    /// A variable Poisson process on the real line.
    /// 
    /// Args:
    ///     tmax (float): upper time bound.
    ///     lambda (func): intensity function object.
    ///     max_lambda (float): upper bound on the intensity.
    ///
    /// Returns:
    ///     events (Tuple[ndarray,ndarray]): events[0] are the timestamps, events[1] are the intensities
    fn variable_poisson_py(
        py: Python, tmax: f64, lambda: PyObject,
        max_lambda: f64) -> (Py<PyArray1<f64>>,Py<PyArray1<f64>>)
    {
        let compute = |x: f64| {
            let args = (x,);
            let obj: PyObject = lambda.call1(py, args).unwrap();
            let res: f64 = obj.extract::<f64>(py).unwrap();
            res
        };

        let (snder, recver) = std::sync::mpsc::sync_channel::<f64>(1);
        let (backsnder, backrcver) = std::sync::mpsc::sync_channel::<f64>(1);
        let backrcver = std::sync::Arc::from(std::sync::Mutex::from(backrcver));
        let callback = move |x: f64| {
            let backrcver = backrcver.lock().unwrap();
            snder.send(x).unwrap();
            let message: Result<f64,_> = backrcver.recv();
            message.unwrap()
        };

        let handle = thread::spawn(move || {
            variable_poisson(tmax, &callback, max_lambda)
        });

        recver.iter().for_each(|x| {
            let intens = compute(x);
            backsnder.send(intens).unwrap();
        });

        let elements = handle.join().unwrap();
        let times: Array1<f64> = elements.timestamps;
        let inten: Array1<f64> = elements.intensities;
        let times = times.to_pyarray(py).to_owned();
        let inten = inten.to_pyarray(py).to_owned();
        (times, inten)
    }

    #[pyfn(module, "hawkes_exp")]
    /// A Hawkes process on the real line wit
    
fn temporal(_py: Python, module: &PyModule) -> PyResult<()> {
    
    #[pyfn(module, "poisson_process")]
    /// Simulate a homogeneous, constant-intensity Poisson process.
    /// 
    /// Args:
    ///     tmax (float): upper time bound.
    ///     lambda (float): base intensity.
    /// 
    /// Returns:
    ///     Process timestamps.
    fn poisson_process_py(py: Python, tmax: f64, lambda: f64) -> Py<PyArray1<f64>> {
        let arr = poisson_process(tmax, lambda);
        arr.to_pyarray(py).to_owned()
    }

    #[pyfn(module, "variable_poisson")]
    /// A variable Poisson process on the real line.
    /// 
    /// Args:
    ///     tmax (float): upper time bound.
    ///     lambda (func): intensity function object.
    ///     max_lambda (float): upper bound on the intensity.
    ///
    /// Returns:
    ///     events (Tuple[ndarray,ndarray]): events[0] are the timestamps, events[1] are the intensities
    fn variable_poisson_py(
        py: Python, tmax: f64, lambda: PyObject,
        max_lambda: f64) -> (Py<PyArray1<f64>>,Py<PyArray1<f64>>)
    {
        let compute = |x: f64| {
            let args = (x,);
            let obj: PyObject = lambda.call1(py, args).unwrap();
            let res: f64 = obj.extract::<f64>(py).unwrap();
            res
        };

        let (snder, recver) = std::sync::mpsc::sync_channel::<f64>(1);
        let (backsnder, backrcver) = std::sync::mpsc::sync_channel::<f64>(1);
        let backrcver = std::sync::Arc::from(std::sync::Mutex::from(backrcver));
        let callback = move |x: f64| {
            let backrcver = backrcver.lock().unwrap();
            snder.send(x).unwrap();
            let message: Result<f64,_> = backrcver.recv();
            message.unwrap()
        };

        let handle = thread::spawn(move || {
            variable_poisson(tmax, &callback, max_lambda)
        });

        recver.iter().for_each(|x| {
            let intens = compute(x);
            backsnder.send(intens).unwrap();
        });

        let elements = handle.join().unwrap();
        let times: Array1<f64> = elements.timestamps;
        let inten: Array1<f64> = elements.intensities;
        let times = times.to_pyarray(py).to_owned();
        let inten = inten.to_pyarray(py).to_owned();
        (times, inten)
    }

    #[pyfn(module, "hawkes_exp")]
    /// A Hawkes process on the real line with an exponential kernel.
    /// 
    /// Args:
    ///     tmax (float): temporal horizon.
    ///     alpha (float): jump size
    ///     beta (float): decay parameter.
    ///     lambda0 (float): base, background intensity.
    /// Returns:
    ///     events (Tuple[ndarray,ndarray]):
    ///         events[0] are the timestamps,
    ///         events[1] are the intensities
    fn hawkes_exp_py(
        py: Python, tmax: f64,
        alpha: f64, beta: f64, lambda0: f64
        ) -> (Py<PyArray1<f64>>, Py<PyArray1<f64>>)
    {
        let events = hawkes_exponential(
            tmax, alpha, beta, lambda0);
        let timestamps = events.timestamps.to_pyarray(py);
        let intensities = events.intensities.to_pyarray(py);
        (timestamps.to_owned(), intensities.to_owned())
    }

 
    /// Simulate a batch of Hawkes-exponential trajectories.
    /// Use this instead of a Python loop.
    /// 
    /// Args:
    ///     tmax (float): temporal horizon.
    ///     alpha (float): jump size
    ///     beta (float): decay parameter.
    ///     lambda0 (float): base, background intensity.
    ///     num_samples (int): number of samples
    #[pyfn(module, "batch_hawkes_exp")]
    fn batch_hawkes_exp(
        py: Python,
        tmax: f64, 
        alpha: f64, beta: f64, lambda0: f64,
        num_samples: usize) -> &PyList
    {
        let model = hawkes::ExpHawkes::new(alpha, beta, lambda0);
        let trajs: Vec<TimeProcessResult> = model.batch_sample(tmax, num_samples);
        convert_vec_results_to_pyarray_list(py, trajs)
    }

    Ok(())
}