code: def forward(
      model: Model[InT, OutT], X1_X2: InT, is_train: bool
  ) -> Tuple[OutT, Callable]:
      X1, X2 = X1_X2
      W = cast(Floats2d, model.get_param("W"))
      diff = X1 - X2
      square_diff = diff**2
      total = (W * square_diff).sum(axis=1)
      sim, bp_sim = inverse(total)
  
      def backprop(d_sim: OutT) -> InT:
          d_total = bp_sim(d_sim)
          d_total = model.ops.reshape2f(d_total, -1, 1)
          model.inc_grad("W", (d_total * square_diff).sum(axis=0))
          d_square_diff = W * d_total
          d_diff = 2 * d_square_diff * diff
          return (d_diff, -d_diff)
  
      return sim, backprop
  
file_path: thinc/thinc/layers/cauchysimilarity.py
imports: from typing import Callable, Optional, Tuple, cast
  from ..config import registry
  from ..model import Model
  from ..types import Floats1d, Floats2d
  from ..util import get_width
label_desc: Use image node_id 2 for calling a global function with example usage: forward(model, X1_X2, is_train) and returns: sim,  backprop, d_diff,  unknown