code: def forward(
      model: Model[InT, OutT], Xnest: InT, is_train: bool
  ) -> Tuple[OutT, Callable]:
      layer: Model[InnerInT, InnerOutT] = model.layers[0]
      Xflat = _flatten(Xnest)
      Yflat, backprop_layer = layer(Xflat, is_train)
      # Get the split points. We want n-1 splits for n items.
      arr = layer.ops.asarray1i([len(x) for x in Xnest[:-1]])
      splits = arr.cumsum()
      Ynest = layer.ops.xp.split(Yflat, splits, axis=0)
  
      def backprop(dYnest: OutT) -> InT:
          dYflat = model.ops.flatten(dYnest)  # type: ignore[arg-type, var-annotated]
          # type ignore necessary for older versions of Mypy/Pydantic
          dXflat = backprop_layer(dYflat)
          dXnest = layer.ops.xp.split(dXflat, splits, axis=-1)
          return dXnest
  
      return Ynest, backprop
  
imports: from typing import Any, Callable, List, Optional, Sequence, Tuple, TypeVar, cast
  from ..config import registry
  from ..model import Model
  from ..types import ArrayXd, ListXd
label_desc: Use image node_id 2 for calling a global function with example usage: forward(model, Xnest, is_train) and returns: Ynest,  backprop, dXnest