//clika-runtime/io.clika.runtime/Ops/convTranspose2d

convTranspose2d

[common]
fun convTranspose2d(input: Tensor, weight: Tensor, bias: Tensor? = null, stride: LongArray = longArrayOf(1, 1), padding: LongArray = longArrayOf(0, 0, 0, 0), outputPadding: LongArray = longArrayOf(0, 0), groups: Long = 1, dilation: LongArray = longArrayOf(1, 1), activation: Activation = Activation.IDENTITY): Tensor

convTranspose2d(input: Tensor, weight: Tensor, bias: Tensor? = null, stride: LongArray = longArrayOf(1, 1), padding: LongArray = longArrayOf(0, 0, 0, 0), outputPadding: LongArray = longArrayOf(0, 0), groups: Long = 1L, dilation: LongArray = longArrayOf(1, 1), activation: Activation = Activation.IDENTITY): the conv_transpose2d operator. 2-D transposed (fractionally-strided) convolution (learnable upsampling). Layout is channels-last; the WEIGHT is the transpose-flip of conv's: input-channel-first [C_in, K.., O/groups]; C_in == weight[0] and C_out == weight[-1] * groups (the cuDNN-BackwardData / ONNX convention, NOT conv's OHWI). padding is per-side: two entries (lo, hi) per spatial dim. output_padding grows only the output's high side.