code: def recursive_copy(x, clone=None, detach=None, retain_grad=None):
      """
      Copies a reference to a tensor, or an object that contains tensors,
      optionally detaching and cloning the tensor(s).  If retain_grad is
      true, the original tensors are marked to have grads retained.
      """
      if not clone and not detach and not retain_grad:
          return x
      if isinstance(x, torch.Tensor):
          if retain_grad:
              if not x.requires_grad:
                  x.requires_grad = True
              x.retain_grad()
          elif detach:
              x = x.detach()
          if clone:
              x = x.clone()
          return x
      # Only dicts, lists, and tuples (and subclasses) can be copied.
      if isinstance(x, dict):
          # We changed this part of the code where we also pass retain_grad.
          return type(x)(
              {
                  k: recursive_copy(v, retain_grad=retain_grad)
                  for k, v in x.items()
              }
          )
      elif isinstance(x, (list, tuple)):
          return type(x)(
              [recursive_copy(v, retain_grad=retain_grad) for v in x]
          )
      else:
          assert (
              False
          ), f"Unknown type {type(x)} cannot be broken into tensors."
  
imports: import contextlib
  import copy
  import inspect
  from collections import OrderedDict
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: recursive_copy(x, clone, detach, retain_grad) and returns: x, x, type, type