code: def forward(
      model: Model, X: Any, is_train: bool
  ) -> Tuple[Any, Callable]:
      """Return the output of the wrapped PyTorch model for the given input,
      along with a callback to handle the backward pass.
      """
      convert_inputs = model.attrs["convert_inputs"]
      convert_outputs = model.attrs["convert_outputs"]
  
      Xtorch, get_dX = convert_inputs(model, X, is_train)
      Ytorch, torch_backprop = model.shims[0](Xtorch, is_train)
      Y, get_dYtorch = convert_outputs(model, (X, Ytorch), is_train)
  
      def backprop(dY: Any) -> Any:
          dYtorch = get_dYtorch(dY)
          dXtorch = torch_backprop(dYtorch)
          dX = get_dX(dXtorch)
          return dX
  
      return Y, backprop
  
file_path: thinc/thinc/layers/pytorchwrapper.py
imports: from typing import Any, Callable, Dict, Optional, Tuple, cast
  from ..compat import torch
  from ..config import registry
  from ..model import Model
  from ..shims import PyTorchGradScaler, PyTorchShim
  from ..types import ArgsKwargs, Floats3d, Padded
  from ..util import convert_recursive, is_torch_array, is_xp_array, partial, torch2xp, xp2torch
label_desc: Use image node_id 5 for calling a global function with example usage: forward(model, X, is_train) and returns: Y,  backprop, dX