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Linear

Linear(in_features, out_features, bias=True, device=None, dtype=None, *, activation=None)

``y = x @ weight.T + bias`` over a ``[out_features, in_features]``
weight and, with ``bias=True``, a ``[out_features]`` bias.

Args:
    in_features: the size of each input sample.
    out_features: the size of each output sample.
    bias: declare the bias slot.
    device: where the layer's tensors live (a device or its string
        form); ``None`` declares on the cpu.
    dtype: the weight dtype the layer declares; ``None`` declares the
        default dtype (:func:`~clika_runtime.get_default_dtype`). A
        ``load_state_dict`` casts the checkpoint to the declared dtype;
        ``assign=True`` adopts the checkpoint's own dtype instead.
    activation: a fused epilogue applied to every output (``"gelu"``,
        ``"silu"``, ... or an :class:`~clika_runtime.Activation`
        value); ``None`` for none.

__init__​

__init__(self, in_features: 'int', out_features: 'int', bias: 'bool' = True, device: 'Device | str | None' = None, dtype: 'DtypeLike | None' = None, *, activation: 'ActivationLike' = None) -> 'None'

Initialize self. See help(type(self)) for accurate signature.

extra_repr​

extra_repr(self) -> 'str'

extra_repr() -> str

One line of per-class detail for :meth:__repr__; a layer prints its geometry here (in_features=64, out_features=256).

forward​

forward(self, input: 'Tensor') -> 'Tensor'

forward(input) -> Tensor

[*, in_features] to [*, out_features].

from_weights​

from_weightsfrom_weights(weight, bias=None) -> Linear

from_weights(weight, bias=None) -> Linear

Construct FROM tensors: geometry, dtype, and device read off weight ([out_features, in_features]); an optional bias ([out_features]) binds beside it.

set_weights​

set_weights(self, weight: 'Tensor', bias: 'Tensor | None' = None) -> 'None'

set_weights(weight, bias=None) -> None

Bind the declared slots directly; raises RuntimeError on a shape mismatch.