code: def tensor_to_gradcheck_var(
      tensor: Tensor,
      dtype: Dtype = torch.float64,
      requires_grad: bool = True,
  ) -> Union[Tensor, str]:
      """Convert the input tensor to a valid variable to check the gradient.
  
      `gradcheck` needs 64-bit floating point and requires gradient.
      """
      if not torch.is_tensor(tensor):
          raise AssertionError(type(tensor))
      return tensor.requires_grad_(requires_grad).type(dtype)
  
imports: import importlib.util
  import math
  from abc import ABC, abstractmethod
  from copy import deepcopy
  from itertools import product
  from typing import Any, Callable, Dict, Iterator, Optional, Sequence, Tuple, TypeVar, Union
  import torch
  from torch.autograd import gradcheck
  from torch.testing import assert_close
  from kornia.core import Device, Dtype, Tensor, eye, tensor
label_desc: Use image node_id 5 for calling a global function with example usage: tensor_to_gradcheck_var(tensor, dtype, requires_grad) and returns: tensor