Zip

block_graph::builtin

Struct Zip 

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pub struct Zip<A> { /* private fields */ }
Expand description

wraps to apply each function

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impl<A> Zip<A>

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pub fn inner(&self) -> &A

references the inner value

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pub fn inner_mut(&mut self) -> &mut A

references the inner value

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pub fn into_inner(self) -> A

returns the inner value

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pub fn new(inner: A) -> Self

Trait Implementations§

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impl<A: AI<K, S>, K, S, B: AI<L, T>, L, T, C: AI<M, U>, M, U, D: AI<N, V>, N, V, E: AI<O, W>, O, W, F: AI<P, X>, P, X, G: AI<Q, Y>, Q, Y, H: AI<R, Z>, R, Z> AI<(K, L, M, N, O, P, Q, R), (S, T, U, V, W, X, Y, Z)> for Zip<(A, B, C, D, E, F, G, H)>

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fn forward( &self, (K, L, M, N, O, P, Q, R): (K, L, M, N, O, P, Q, R), ) -> (S, T, U, V, W, X, Y, Z)

applies to the input
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fn forward_mut( &mut self, (K, L, M, N, O, P, Q, R): (K, L, M, N, O, P, Q, R), ) -> (S, T, U, V, W, X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<M, T>, M, T, B: AI<N, U>, N, U, C: AI<O, V>, O, V, D: AI<P, W>, P, W, E: AI<Q, X>, Q, X, F: AI<R, Y>, R, Y, G: AI<S, Z>, S, Z> AI<(M, N, O, P, Q, R, S), (T, U, V, W, X, Y, Z)> for Zip<(A, B, C, D, E, F, G)>

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fn forward( &self, (M, N, O, P, Q, R, S): (M, N, O, P, Q, R, S), ) -> (T, U, V, W, X, Y, Z)

applies to the input
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fn forward_mut( &mut self, (M, N, O, P, Q, R, S): (M, N, O, P, Q, R, S), ) -> (T, U, V, W, X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<O, U>, O, U, B: AI<P, V>, P, V, C: AI<Q, W>, Q, W, D: AI<R, X>, R, X, E: AI<S, Y>, S, Y, F: AI<T, Z>, T, Z> AI<(O, P, Q, R, S, T), (U, V, W, X, Y, Z)> for Zip<(A, B, C, D, E, F)>

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fn forward(&self, (O, P, Q, R, S, T): (O, P, Q, R, S, T)) -> (U, V, W, X, Y, Z)

applies to the input
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fn forward_mut( &mut self, (O, P, Q, R, S, T): (O, P, Q, R, S, T), ) -> (U, V, W, X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<Q, V>, Q, V, B: AI<R, W>, R, W, C: AI<S, X>, S, X, D: AI<T, Y>, T, Y, E: AI<U, Z>, U, Z> AI<(Q, R, S, T, U), (V, W, X, Y, Z)> for Zip<(A, B, C, D, E)>

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fn forward(&self, (Q, R, S, T, U): (Q, R, S, T, U)) -> (V, W, X, Y, Z)

applies to the input
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fn forward_mut(&mut self, (Q, R, S, T, U): (Q, R, S, T, U)) -> (V, W, X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<S, W>, S, W, B: AI<T, X>, T, X, C: AI<U, Y>, U, Y, D: AI<V, Z>, V, Z> AI<(S, T, U, V), (W, X, Y, Z)> for Zip<(A, B, C, D)>

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fn forward(&self, (S, T, U, V): (S, T, U, V)) -> (W, X, Y, Z)

applies to the input
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fn forward_mut(&mut self, (S, T, U, V): (S, T, U, V)) -> (W, X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<U, X>, U, X, B: AI<V, Y>, V, Y, C: AI<W, Z>, W, Z> AI<(U, V, W), (X, Y, Z)> for Zip<(A, B, C)>

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fn forward(&self, (U, V, W): (U, V, W)) -> (X, Y, Z)

applies to the input
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fn forward_mut(&mut self, (U, V, W): (U, V, W)) -> (X, Y, Z)

applies to the input, possibly updating internal caches
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impl<A: AI<W, Y>, W, Y, B: AI<X, Z>, X, Z> AI<(W, X), (Y, Z)> for Zip<(A, B)>

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fn forward(&self, (W, X): (W, X)) -> (Y, Z)

applies to the input
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fn forward_mut(&mut self, (W, X): (W, X)) -> (Y, Z)

applies to the input, possibly updating internal caches
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impl<A: Clone> Clone for Zip<A>

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fn clone(&self) -> Zip<A>

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl<A: Debug> Debug for Zip<A>

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

Formats the value using the given formatter. Read more
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impl<A: Decompose> Decompose for Zip<A>

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type Decomposition = <A as Decompose>::Decomposition

the decomposed type
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fn compose(decomposition: Self::Decomposition) -> Self

recreates from the decomposition
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fn decompose(self) -> Self::Decomposition

owned decomposition
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fn decompose_cloned(&self) -> Self::Decomposition

decomposition that copies data
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impl<A: Default> Default for Zip<A>

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fn default() -> Zip<A>

Returns the “default value” for a type. Read more
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impl<'de, A> Deserialize<'de> for Zip<A>
where A: Deserialize<'de>,

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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>
where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more
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impl<A: Op<Output = Y>, Y, B: Op<Output = Z>, Z> Op for Zip<(A, B)>

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type Output = (Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
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fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = X>, X, B: Op<Output = Y>, Y, C: Op<Output = Z>, Z> Op for Zip<(A, B, C)>

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type Output = (X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
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fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = W>, W, B: Op<Output = X>, X, C: Op<Output = Y>, Y, D: Op<Output = Z>, Z> Op for Zip<(A, B, C, D)>

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type Output = (W, X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
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fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = V>, V, B: Op<Output = W>, W, C: Op<Output = X>, X, D: Op<Output = Y>, Y, E: Op<Output = Z>, Z> Op for Zip<(A, B, C, D, E)>

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type Output = (V, W, X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
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fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = U>, U, B: Op<Output = V>, V, C: Op<Output = W>, W, D: Op<Output = X>, X, E: Op<Output = Y>, Y, F: Op<Output = Z>, Z> Op for Zip<(A, B, C, D, E, F)>

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type Output = (U, V, W, X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
Source§

fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = T>, T, B: Op<Output = U>, U, C: Op<Output = V>, V, D: Op<Output = W>, W, E: Op<Output = X>, X, F: Op<Output = Y>, Y, G: Op<Output = Z>, Z> Op for Zip<(A, B, C, D, E, F, G)>

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type Output = (T, U, V, W, X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
Source§

fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
Source§

fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A: Op<Output = S>, S, B: Op<Output = T>, T, C: Op<Output = U>, U, D: Op<Output = V>, V, E: Op<Output = W>, W, F: Op<Output = X>, X, G: Op<Output = Y>, Y, H: Op<Output = Z>, Z> Op for Zip<(A, B, C, D, E, F, G, H)>

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type Output = (S, T, U, V, W, X, Y, Z)

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X
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fn chain<B>(self, b: B) -> Sequential<(Self, B)>
where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation
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fn duplicate(self) -> Duplicate<Self>
where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation
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fn fix_type<Z>(self) -> SetType<Self, Z, Z>
where Self: AI<Z, Z> + Sized,

set type but with the same input and output
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fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>
where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer
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fn forward_fixed<Z>(&self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z
where Self: AI<Z, Z> + Sized,

applies to the input
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fn forward_typed<W, Z>(&self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input
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fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z
where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches
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fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y> ⓘ
where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference
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fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>
where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output
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fn optional(self) -> Option<Self>
where Self: Sized,

creates an optional operation
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fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>
where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer
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fn residual(self) -> Residual<Self>
where Residual<Self>: Op, Self: Sized,

wraps in a residual layer
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fn set_type<W, Z>(self) -> SetType<Self, W, Z>
where Self: AI<W, Z> + Sized,

sets the input output types
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fn to_each(self) -> Map<Self>
where Map<Self>: Op, Self: Sized,

wraps with a map operation
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fn wrap_inner(self) -> Inner<Self>
where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner
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fn zip<B>(self, b: B) -> Zip<(Self, B)>
where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation
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impl<A> Serialize for Zip<A>
where A: Serialize,

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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>
where __S: Serializer,

Serialize this value into the given Serde serializer. Read more
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impl<W: Wrappable> Wrappable for Zip<W>

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type B = <W as Wrappable>::B

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type With<C: Backend> = Zip<<W as Wrappable>::With<C>>

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impl<A: Copy> Copy for Zip<A>

Auto Trait Implementations§

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impl<A> Freeze for Zip<A>
where A: Freeze,

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impl<A> RefUnwindSafe for Zip<A>
where A: RefUnwindSafe,

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impl<A> Send for Zip<A>
where A: Send,

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impl<A> Sync for Zip<A>
where A: Sync,

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impl<A> Unpin for Zip<A>
where A: Unpin,

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impl<A> UnwindSafe for Zip<A>
where A: UnwindSafe,

Blanket Implementations§

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impl<T> Adaptor<()> for T

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fn adapt(&self)

Adapt the type to be passed to a metric.
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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
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impl<T> CloneToUninit for T
where T: Clone,

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unsafe fn clone_to_uninit(&self, dest: *mut u8)

🔬This is a nightly-only experimental API. (clone_to_uninit)
Performs copy-assignment from self to dest. Read more
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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into<U> for T
where U: From<T>,

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> IntoEither for T

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fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
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fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
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impl<T> Pointable for T

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const ALIGN: usize

The alignment of pointer.
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type Init = T

The type for initializers.
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unsafe fn init(init: <T as Pointable>::Init) -> usize

Initializes a with the given initializer. Read more
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unsafe fn deref<'a>(ptr: usize) -> &'a T

Dereferences the given pointer. Read more
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unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

Mutably dereferences the given pointer. Read more
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unsafe fn drop(ptr: usize)

Drops the object pointed to by the given pointer. Read more
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impl<T> Shortcuts for T
where T: Op + ?Sized,

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fn classification(self) -> Classification<Self>
where Classification<Self>: Op, Self: Sized,

wraps in a classification wrapper
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fn regression(self) -> Regression<Self>
where Regression<Self>: Op, Self: Sized,

wraps in a regression wrapper
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fn wrap(self) -> Wrapped<Self>
where Self: Wrappable,

wraps in a burn wrapper
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impl<T> ToOwned for T
where T: Clone,

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<V, T> VZip<V> for T
where V: MultiLane<T>,

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fn vzip(self) -> V

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impl<T> DeserializeOwned for T
where T: for<'de> Deserialize<'de>,