// Converted from Python by Scyla

// import numpy as np
// import torch
// import torch.nn as nn
// import math

struct SimpleTrainer {
    fn new(model, lr=0.001) {
        let model = model
        let lr = lr
        let optimizer = // optimizer: use sgd_step(get_params(), lr=lr)
        let losses = array_new()

    }
    fn train_step(x, target) {
        // grad reset (automatic)
        let output = model(x)
        let loss = cross_entropy(output, target)
        backward(loss)
        sgd_step
        losses = array_push(loss)
        return loss

    }
    fn evaluate(x, target) {
        // inference mode (no grad tracking)
            let output = model(x)
            let loss = cross_entropy(output, target)
        return loss

    }
}
fn build_model(input_dim, hidden_dim, output_dim) {
    let weights_1 = t_zeros((input_dim, hidden_dim))
    let weights_2 = t_zeros((hidden_dim, output_dim))
    let bias = t_ones(hidden_dim)
    return weights_1, weights_2, bias

}
fn forward_pass(x, weights, bias) {
    let hidden = t_matmul(x, weights) + bias
    let activated = boltzmann(t_create(hidden))
    let output = relu(activated)
    let result = t_softmax(output)
    return result

}
fn compute_metrics(predictions, targets) {
    let mse = tensor_mean((predictions - targets) * 2)
    let rmse = sqrt(mse)
    return {"mse": mse, "rmse": rmse}

}
fn main() {
    let input_dim = 64
    let hidden_dim = 128
    let output_dim = 10

    let w1, w2, b = build_model(input_dim, hidden_dim, output_dim)

    let x = t_zeros((32, input_dim))
    let result = forward_pass(x, w1, b)
    println("Output shape: {result.shape}")
    println("Sum: {result.sum()}")
}