Struct Random

pub struct Random<D: Type> {
    pub from: D::Repr,
    pub to: D::Repr,
}

Fields

from: D::Repr

to: D::Repr

Implementations

impl<D: Type> Random<D>

pub fn new(from: D::Repr, to: D::Repr) -> Self

Trait Implementations

impl<D: Clone + Type> Clone for Random<D>

where D::Repr: Clone,

fn clone(&self) -> Random<D>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<D: Debug + Type> Debug for Random<D>

where D::Repr: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<D, T> Eval<D, Random<T>> for CandleBackend

where D: Device + WithDevice, T: Type, T::Repr: FloatDType,

fn eval(&self, device: &D, primitive: &Random<T>, _: &[Tensor], output: &Tensor)

impl<D: PartialEq + Type> PartialEq for Random<D>

where D::Repr: PartialEq,

fn eq(&self, other: &Random<D>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<D: Type> Primitive for Random<D>

fn clone_boxed(&self) -> Box<dyn Primitive>

fn as_any(&self) -> &dyn Any

fn dot_label(&self) -> String

fn jvp( &self, output: &Tensor, _primals: &[Tensor], _tangents: &[Tensor], ) -> Tensor

fn vjp( &self, _output: &Tensor, _primals: &[Tensor], _cotangent: &Tensor, ) -> Vec<Tensor>

impl<D: Copy + Type> Copy for Random<D>

where D::Repr: Copy,

impl<D: Eq + Type> Eq for Random<D>

where D::Repr: Eq,

impl<D: Type> StructuralPartialEq for Random<D>