You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: conv1d.rs
    path of file: ./repos/burn/crates/burn-core/src/nn/conv
    
    the code of the file until where you have to start completion: use crate as burn;

use crate::config::Config;
use crate::module::Module;
use crate::module::Param;
use crate::nn::{Initializer, PaddingConfig1d};
use crate::tensor::backend::Backend;
use crate::tensor::Tensor;
use burn_tensor::module::conv1d;
use burn_tensor::ops::ConvOptions;
use libm::sqrt;

use super::checks;

/// Configuration to create an [1D convolution](Conv1d) layer.
#[derive(Config, Debug)]
pub struct Conv1dConfig {
    /// The number of input channels.
    pub channels_in: usize,
    /// The number of outpu
    
    pub fn init<B: Backend>(&self, device: &B::Device) -> Conv1d<B> {
        checks::checks_channels_div_groups(self.channels_in, self.channels_out, self.groups);

        let shape = [
            self.channels_out,
            self.channels_in / self.groups,
            self.kernel_size,
        ];

        let fan_in: usize = self.channels_in / self.groups * self.kernel_size;
        let weight = self
            .initializer
            .init_with(shape, Some(fan_in), None, device);
        let mut bias = None;

        if self.bias {
            bias =
                Some(
                    self.initializer
                        .init_with([self.channels_out], Some(fan_in), None, device),
                );
        }

        Conv1d {
            weight: Param::from(weight),
            bias: bias.map(Param::from),
            stride: self.stride,
            kernel_size: self.kernel_size,
            padding: self.padding.clone(),
            dilation: self.dilation,
            groups: self.groups,
        }
    }