Building Models with nn

teeny-core::nn (API docs) provides the Layer trait and a set of standard layers:

• Convolution: conv1d, conv2d, conv3d
• Normalization: batchnorm, groupnorm, instancenorm, layernorm, rmsnorm
• Other: activation, flatten, linear, pad, pool

Every layer implements Layer<I>, with an associated Output type and a call(&self, input: I) -> Self::Output method — this is what gets invoked during tracing to build up a Graph.

Composite models (e.g. teeny-vision::mnist::mnist_lenet5) are just structs composing these layers and implementing Layer themselves by chaining calls to their fields’ call methods — see models/teeny-vision/src/mnist/ for a complete example.

TODO: document the parameter-management/initialization story once it’s stable enough to commit to in the book (constructing layers with random vs. loaded weights, interaction with name scopes).