chela 0.0.2

High-performance Machine Learning, Auto-Differentiation and Tensor Algebra crate for Rust
Documentation 
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use crate::{NdArray, TensorDataType};
use std::cell::RefCell;
use std::rc::Rc;


pub(crate) trait GradientFuncTrait<T: TensorDataType> {
    /// Computes the gradient of this function with respect to its sources using the chain rule.
    ///
    /// # Parameters
    ///
    /// - `grad`: the gradient of the function being differentiated with respect to `self`.
    fn backward(&mut self, grad: &NdArray<T>);

    /// Returns the gradient of the function being differentiated with respect to `self`
    /// if this function is a leaf. Otherwise, returns `None`.
    fn gradient<'a>(&'a self) -> Option<NdArray<'a, T>> {
        None
    }
}


pub(crate) type GradientFunction<T> = Rc<RefCell<dyn GradientFuncTrait<T>>>;