code: def require_grad(x: Any, requires_grad: bool = True) -> Any:
      if (
          isinstance(x, Tensor)
          and x.is_floating_point()
          and not is_torch_sparse_tensor(x)
      ):
          return x.detach().requires_grad_(requires_grad)
      elif isinstance(x, list):
          return [require_grad(v, requires_grad) for v in x]
      elif isinstance(x, tuple):
          return tuple(require_grad(v, requires_grad) for v in x)
      elif isinstance(x, dict):
          return {
              k: require_grad(v, requires_grad) for k, v in x.items()
          }
      return x
  
imports: import time
  from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric.utils import is_torch_sparse_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: require_grad(x, requires_grad) and returns: x, x, unknown, tuple, unknown