---
name: model
type: bundle
status: stable
---

# 1. Overview
A simple Multi-Layer Perceptron (MLP) for regression tasks that maps a 10-dimensional input vector to a scalar output.

# 2. Interface Specification

```yaml:functions
get_model:
  inputs:
    input_dim:
      type: integer
      default: 10
    hidden_dim:
      type: integer
      default: 32
    output_dim:
      type: integer
      default: 1
  outputs:
    model:
      type: object
      description: "An object implementing a forward-pass interface for a neural network."
  behavior: "Creates and returns a neural network model."
```

## 2.1 Model Interface
The returned model object must provide:
*   **forward(input_vector)**: Receives a vector (or batch of vectors) and returns the scalar output (or batch of scalar outputs).

# 3. Functional Requirements (Behavior)
*   **FR-01**: The network shall have 3 linear layers.
*   **FR-02**: The hidden layers shall use ReLU activation functions.
*   **FR-03**: The final layer shall have no activation function (linear regression).

# 4. Non-Functional Requirements (Constraints)
*   **NFR-Framework**: Must use PyTorch (`torch.nn`).
*   **NFR-TypeHinting**: All methods must have type hints.

# 5. Design Contract
*   **Pre-condition**: Input tensor `x` must match `input_dim`.
*   **Post-condition**: Output tensor must match `output_dim`.

# 6. Test Scenarios
| Scenario | Input | Expected Output | Notes |
|----------|-------|-----------------|-------|
| Forward Pass | Tensor `(1, 10)` | Tensor `(1, 1)` | Shape check |
| Batch Pass | Tensor `(16, 10)` | Tensor `(16, 1)` | Batch dimension preserved |
