# `ExTorch.NN.Module`

DSL for defining neural network modules in Elixir.

Use this module to declaratively define neural network architectures
with a PyTorch-inspired syntax. Each module defines layers and a
`forward/2` function.

## Example

    defmodule MyMLP do
      use ExTorch.NN.Module

      deflayer :fc1, ExTorch.NN.Linear, in_features: 784, out_features: 128
      deflayer :relu, ExTorch.NN.ReLU
      deflayer :fc2, ExTorch.NN.Linear, in_features: 128, out_features: 10

      def forward(model, x) do
        x
        |> layer(model, :fc1)
        |> layer(model, :relu)
        |> layer(model, :fc2)
      end
    end

## Usage

    # Fresh model with random weights
    model = MyMLP.new()
    output = MyMLP.forward(model, input)

    # Load pre-trained weights from a TorchScript file
    model = MyMLP.from_jit("model.pt")
    output = MyMLP.forward(model, input)

When loaded via `from_jit/1`, the JIT model's `forward` method is called
directly, using the pre-trained weights. The DSL definition serves as a
structural contract that is validated against the `.pt` file's submodules.

`deflayer` declares a layer at compile time. `layer/3` is a runtime
function that looks up and applies a named layer during forward.

# `create`

```elixir
@callback create(keyword()) :: ExTorch.NN.Layer.t()
```

Layer behaviour that nn layer modules must implement.

# `deflayer`
*macro* 

Declare a layer in the module definition.

## Arguments
  - `name` - Atom name for the layer.
  - `layer_module` - The layer type module (e.g., `ExTorch.NN.Linear`).
  - `opts` - Keyword options passed to the layer's `create/1` callback.

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*Consult [api-reference.md](api-reference.md) for complete listing*