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Conv

A grouped, padded, dilated convolution over channels-last input.

__init__​

__init__(self, in_channels: 'int', out_channels: 'int', kernel: 'Sequence[int]', *, stride: 'Sequence[int] | None' = None, padding: 'Sequence[int] | None' = None, dilation: 'Sequence[int] | None' = None, groups: 'int' = 1, bias: 'bool' = True, mode: 'PadMode | None' = None, value: 'float | None' = None, activation: 'Activation | None' = None, dtype: 'DataType | None' = None, device: 'Device | None' = None) -> 'None'

Declare the layer's slots. Unset stride/padding/ dilation mean 1s/0s/1s; mode picks the padding semantics (constant by default, with value as the constant); activation fuses an epilogue into every forward.

extra_repr​

extra_repr(self) -> 'str'

One line of per-class detail for :meth:__repr__ (a layer prints its geometry here).

forward​

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

Subclasses define the computation here; call the module itself (m(x)), not forward directly.

set_weights​

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

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