code: def stop_gradient(
      x: Optional[torch.Tensor],
      /,
      *,
      preserve_type: bool = True,
      out: Optional[torch.Tensor] = None,
  ):
      if is_variable(x) and preserve_type:
          if x.grad_fn:
              x = x.detach()
              x.requires_grad = True
          elif x.grad:
              x.grad.data.zero_()
          return x
      return x.detach()
  
file_path: ivy/ivy/functional/backends/torch/gradients.py
imports: import torch
  from typing import Optional, Callable, Sequence, Union
  import ivy
  from ivy.func_wrapper import outputs_to_ivy_arrays, inputs_to_native_arrays
  from ivy.functional.ivy.gradients import _get_required_float_variables, _get_y_and_ret_idxs, _get_native_y, _set_duplicates, _process_func_ret_and_grads
label_desc: Use image node_id 7 for calling a global function with example usage: stop_gradient() and returns: x, x