Function launch_conv 

pub fn launch_conv<R: Runtime, MP: MatmulPrecision, Alg: Algorithm, const N_SPATIAL: usize>(
    client: &ComputeClient<R::Server>,
    input: &MatmulInputHandleRef<'_, R>,
    weight: &MatmulInputHandleRef<'_, R>,
    bias: &Option<TensorHandleRef<'_, R>>,
    out: &TensorHandleRef<'_, R>,
    args: ConvolutionArgs<N_SPATIAL>,
) -> Result<(), ConvSetupError>
where
    <<Alg as Algorithm>::Args as MatmulArgs>::Input<<<MP as MatmulPrecision>::Lhs as MatrixPrecision>::Global, <<MP as MatmulPrecision>::Rhs as MatrixPrecision>::Global, <<MP as MatmulPrecision>::Acc as MatrixPrecision>::Global>: ConcreteInputsFactory,
    <<Alg as Algorithm>::Args as MatmulArgs>::Output<<<MP as MatmulPrecision>::Acc as MatrixPrecision>::Global>: ConcreteOutputFactory,

Perform an n-dimensional convolution using the implicit GEMM (im2col) algorithm, using cubecl tiling matmul components, using the specified algorithm.

• input - The input feature map, layout should be [batches, depth, height, width, in_channels]
• weight - The weights (filter) applied to each kernel, layout should be [out_channels, kernel_d, kernel_h, kernel_w, in_channels]
• out - The output feature map, layout should be [batches, out_depth, out_height, out_width, out_channels]
• bias - The bias added to each out channel
• options - The options to use for the convolution