code: def execute_with_gradients(
      func,
      xs: torch.Tensor,
      /,
      *,
      retain_grads: bool = False,
      xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
      ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
  ):
      # Conversion of required arrays to float variables and duplicate index chains
      (
          xs,
          xs_grad_idxs,
          xs1,
          required_duplicate_index_chains,
          _,
      ) = _get_required_float_variables(xs, xs_grad_idxs)
      func_ret = func(xs)
      xs = xs1
  
      # Getting the relevant outputs from the function return for gradient calculation
      ret_grad_idxs, y, ret_idxs = _get_y_and_ret_idxs(
          func_ret, ret_grad_idxs, create_var=True
      )
  
      if isinstance(y, ivy.NativeArray):
          # Gradient calculation for a single output
          grads = _set_duplicates(
              _grad_func(torch.clone(y), xs, retain_grads),
              required_duplicate_index_chains,
          )
      else:
          # Gradient calculation for multiple outputs
          # ToDo: use functorch.jacrev if it fixes the issue with broken memory reference
          y = _get_native_y(y)
          grad_arr_idxs = ivy.nested_argwhere(
              y, lambda x: ivy.is_native_array(x)
          )
          grad_arr_values = ivy.multi_index_nest(y, grad_arr_idxs)
          grads_ = [
              _grad_func(torch.clone(arr_value), xs, retain_grads)
              for arr_value in grad_arr_values
          ]
          grads = grads_
          if isinstance(ret_idxs, list) and len(ret_idxs):
              grads = {
                  ret_idxs[i]: _set_duplicates(
                      grad, required_duplicate_index_chains
                  )
                  for i, grad in enumerate(grads_)
              }
  
      # Stop further gradient propagation if not retaining gradients
      return _process_func_ret_and_grads(func_ret, grads, retain_grads)
  
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 5 for calling a global function with example usage: execute_with_gradients() and returns: _process_func_ret_and_grads