code: 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},
      )
  
file_path: spacy/spacy/ml/staticvectors.py
imports: import warnings
  from typing import Callable, List, Optional, Sequence, Tuple, cast
  from thinc.api import Model, Ops, registry
  from thinc.initializers import glorot_uniform_init
  from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
  from thinc.util import partial
  from ..attrs import ORTH
  from ..errors import Errors, Warnings
  from ..tokens import Doc
  from ..vectors import Mode, Vectors
  from ..vocab import Vocab
label_desc: Use image node_id 1 for calling a global function with example usage: StaticVectors(nO, nM) and returns: Model