2nd step ChatGPT output

#include <iostream>
#include <xtensor/xarray.hpp>
#include <xtensor/xrandom.hpp>
#include <xtensor/xview.hpp>
#include <xtensor/xio.hpp>
#include <xtensor/xlinalg.hpp>

class LayerDense {
public:
    LayerDense(size_t n_inputs, size_t n_neurons);

    xt::xarray<float> forward(const xt::xarray<float>& inputs);

private:
    xt::xarray<float> weights;
    xt::xarray<float> biases;
};

LayerDense::LayerDense(size_t n_inputs, size_t n_neurons) {
    weights = 0.10 * xt::random::randn<float>({n_inputs, n_neurons});
    biases = xt::zeros<float>({1, n_neurons});
}

xt::xarray<float> LayerDense::forward(const xt::xarray<float>& inputs) {
    return xt::linalg::dot(inputs, weights) + biases;
}

int main() {
    // Define input data
    xt::xarray<float> inputs = {
        {1., 1., 3., 2.5},
        {2., 5., -1., 2.},
        {-1.5, 2.7, 3.3, -0.8}
    };

    // Define weights and biases for the layers
    xt::xarray<float> weights1 = {
        {0.2, 0.8, -0.5, 1.},
        {0.5, -0.91, 0.26, -0.5},
        {-0.26, -0.27, 0.17, 0.87}
    };

    xt::xarray<float> weights2 = {
        {0.2, 0.8, -0.5, 1.},
        {0.5, -0.91, 0.26, -0.5},
        {-0.26, -0.27, 0.17, 0.87}
    };

    xt::xarray<float> biases1 = {2., 3., 0.5};
    xt::xarray<float> biases2 = {1., .4, 4};

    // Create the layers
    LayerDense layer1(inputs.shape()[1], biases1.shape()[0]);
    LayerDense layer2(layer1.forward(inputs).shape()[1], biases2.shape()[0]);

    // Perform forward pass through the layers
    xt::xarray<float> layer1_output = layer1.forward(inputs);
    xt::xarray<float> layer2_output = layer2.forward(layer1_output);

    // Display the output
    std::cout << "Layer 1 output:\n" << layer1_output << std::endl;
    std::cout << "Layer 2 output:\n" << layer2_output << std::endl;

    return 0;
}