def test_no_constraint_if_within_norm():
    """
    Test that weights within the max_norm are not altered after forward pass.
    """
    in_features = 3
    out_features = 2
    max_norm = 2.0
    layer = LinearWithConstraint(in_features, out_features, max_norm)

    # Initialize weights with norms less than or equal to max_norm
    with torch.no_grad():
        layer.weight.data = torch.tensor([[1.0, 0.0, 0.0],
                                         [0.0, 1.0, 0.0]])

    input = torch.randn(1, in_features)
    original_weights = layer.weight.data.clone()
    layer(input)

    # Weights should remain unchanged
    assert torch.allclose(layer.weight.data, original_weights), "Weights were altered despite being within max_norm"

    Test that weights within the max_norm are not altered after forward pass.
    
test/unit_tests/models/test_modules.py