code: def Sigmoid(
      nO: Optional[int] = None,
      nI: Optional[int] = None,
      *,
      init_W: Optional[Callable] = None,
      init_b: Optional[Callable] = None,
  ) -> Model[InT, OutT]:
      """A dense layer, followed by a sigmoid (logistic) activation function. This
      is usually used instead of the Softmax layer as an output for multi-label
      classification.
      """
      if init_W is None:
          init_W = zero_init
      if init_b is None:
          init_b = zero_init
      return Model(
          "sigmoid",
          forward,
          init=partial(init, init_W, init_b),
          dims={"nO": nO, "nI": nI},
          params={"W": None, "b": None},
      )
  
file_path: thinc/thinc/layers/sigmoid.py
imports: from typing import Callable, Optional, Tuple, cast
  from ..config import registry
  from ..initializers import zero_init
  from ..model import Model
  from ..types import Floats1d, Floats2d
  from ..util import get_width, partial
label_desc: Use image node_id 1 for calling a global function with example usage: Sigmoid(nO, nI) and returns: Model