code: def convert_rnn_inputs(model: Model, Xp: Padded, is_train: bool):
      shim = cast(PyTorchShim, model.shims[0])
      size_at_t = Xp.size_at_t
      lengths = Xp.lengths
      indices = Xp.indices
  
      def convert_from_torch_backward(d_inputs: ArgsKwargs) -> Padded:
          dX = torch2xp(d_inputs.args[0])
          return Padded(dX, size_at_t, lengths, indices)  # type: ignore
  
      output = ArgsKwargs(
          args=(
              xp2torch(Xp.data, requires_grad=True, device=shim.device),
              None,
          ),
          kwargs={},
      )
      return output, convert_from_torch_backward
  
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 8 for calling a global function with example usage: convert_rnn_inputs(model, Xp, is_train) and returns: output,  convert_from_torch_backward, Padded