def StaticVectors(
    nO: Optional[int] = None,
    nM: Optional[int] = None,
    *,
    dropout: Optional[float] = None,
    init_W: Callable = glorot_uniform_init,
    key_attr: str = "ORTH"
) -> Model[List[Doc], Ragged]:
    """Embed Doc objects with their vocab's vectors table, applying a learned
    linear projection to control the dimensionality. If a dropout rate is
    specified, the dropout is applied per dimension over the whole batch.
    """
    if key_attr != "ORTH":
        warnings.warn(Warnings.W125, DeprecationWarning)
    return Model(
        "static_vectors",
        forward,
        init=partial(init, init_W),
        params={"W": None},
        attrs={"key_attr": key_attr, "dropout_rate": dropout},
        dims={"nO": nO, "nM": nM},
    )
Embed Doc objects with their vocab's vectors table, applying a learned
    linear projection to control the dimensionality. If a dropout rate is
    specified, the dropout is applied per dimension over the whole batch.
    
spacy/ml/staticvectors.py