code: def _res_to_torch(r, ctx):
      """Convert results from unwrapped execution to torch."""
      if isinstance(r, dict):
          return r
      if isinstance(r, (list, tuple)):
          return type(r)(_res_to_torch(t, ctx) for t in r)
      return torch.as_tensor(r, device=ctx.torch_device)
  
file_path: pennylane/pennylane/workflow/interfaces/torch.py
imports: import inspect
  import logging
  import numpy
  import torch
  import torch.utils._pytree
  import pennylane
label_desc: Use image node_id 4 for calling a global function with example usage: _res_to_torch(r, ctx) and returns: torch, r, type