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: convmixer.rs
    path of file: ./repos/tch-rs/src/vision
    
    the code of the file until where you have to start completion: //! ConvMixer implementation.
//!
//! See "Patches Are All You Need?", submitted to ICLR'21
//! <https://openreview.net/forum?id=TVHS5Y4dNvM>
use crate::nn;

fn block(p: nn::Path, dim: i64, kernel_size: i64) -> impl nn::ModuleT {
    let conv2d_cfg = nn::ConvConfig { groups: dim, ..Default::default() };
    let p_fn = &(&p / "0") / "fn";
    let conv1 =
        crate::vision::efficientnet::conv2d_same(&p_fn / "0", dim, dim, kernel_size, conv2d_cfg);
    let bn1 = nn::batch_norm2d(&p_fn / "2", dim, Default::default());
    let conv2 
    
fn block(p: nn::Path, dim: i64, kernel_size: i64) -> impl nn::ModuleT {
    let conv2d_cfg = nn::ConvConfig { groups: dim, ..Default::default() };
    let p_fn = &(&p / "0") / "fn";
    let conv1 =
        crate::vision::efficientnet::conv2d_same(&p_fn / "0", dim, dim, kernel_size, conv2d_cfg);
    let bn1 = nn::batch_norm2d(&p_fn / "2", dim, Default::default());
    let conv2 = nn::conv2d(&p / "1", dim, dim, 1, Default::default());
    let bn2 = nn::batch_norm2d(&p / "3", dim, Default::default());
    nn::func_t(move |xs, train| {
        let ys = xs.apply(&conv1).gelu("none").apply_t(&bn1, train);
        (xs + ys).apply(&conv2).gelu("none").apply_t(&bn2, train)
    })
}