Struct ReflectionPad 

pub struct ReflectionPad { /* private fields */ }

Reflection padding, covering ReflectionPad1d/2d/3d. Matches PyTorch’s own constraint that each side’s padding be strictly less than the axis’s extent (checked before launch, in reflect_source’s doc); see edge_pad for the composition and its exact gradient.

Implementations

impl ReflectionPad

pub fn new(pads: impl IntoIterator<Item = (Axis, usize, usize)>) -> Result<Self>

Trait Implementations

impl Module for ReflectionPad

fn output_shape(&self, input: &Shape) -> Result<Shape>

fn build( &mut self, input: &Shape, _device: &Device, _seed: u64, ) -> Result<Shape>

Infer extents, validate contracts, allocate and initialize parameters; never run forward. Rebuilding with compatible extents/device preserves parameters and ignores the new seed.

fn forward(&self, input: &Tensor) -> Result<Tensor>

Evaluation semantics: no mode flag on self, no randomness beyond what forward itself already defines. This never changes with the arrival of forward_training.

fn forward_training( &self, input: &Tensor, pass: &mut TrainingPass, ) -> Result<Tensor>

Training-mode forward pass, driven by an explicit TrainingPass rather than a flag on self. The default implementation ignores pass and calls Module::forward, which is exactly today’s behavior for every module without a train/eval distinction. Override this (never forward, which must stay evaluation semantics) to add explicit randomness or a train/eval difference, such as Dropout. A container that holds child modules (Sequential, or any future one) overrides this to thread the same pass through every child in order, so their draws stay distinct within one step.

fn named_parameters(&self) -> Vec<(String, Parameter)>

Structural paths identify slots; ParamId identifies shared storage within this process.

fn parameter(&self, name: &str) -> Result<Parameter>

fn parameters(&self) -> Vec<Parameter>

fn zero_grad(&self)

fn named_states(&self) -> Vec<(String, State)>

Persistent non-parameter tensors this module owns across training steps – BatchNorm-style running statistics. Same slot-path convention as Module::named_parameters: a container (Sequential or any future one) aggregates every child’s own states under "{index}.{name}". Defaults empty, exactly like named_parameters, since most modules have no persistent state. A State here is never written directly; only TrainingPass::commit writes one, after staging in forward_training.

fn state(&self, name: &str) -> Result<State>

fn states(&self) -> Vec<State>