code: def execute_with_gradients(
      func,
      xs: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      retain_grads: bool = False,
      xs_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
      ret_grad_idxs: Sequence[Sequence[Union[str, int]]] = ((0,),),
  ) -> Tuple[ivy.Array, ivy.Array]:
      """Call function func with input of xs variables, and return the function
      result func_ret and the gradients of each output variable w.r.t each input
      variable,
  
      Parameters
      ----------
      func
          Function for which we compute the gradients of the output with respect to xs
          input.
      xs
          Variables for which to compute the function gradients with respective to. This
          can be a single array or an arbitrary nest of arrays.
      retain_grads
          Whether to retain the gradients of the returned values. (Default value = False)
      xs_grad_idxs
          Indices of the input arrays to compute gradients with respect to. If None,
          gradients are returned with respect to all input arrays. If ``xs`` is an
          ``ivy.Array`` or ``ivy.Container``, the default value is ``None``, otherwise the
          default value is ``[[0]]``.
      ret_grad_idxs
          Indices of the returned arrays for which to return computed gradients. If None,
          gradients are returned for all returned arrays. If the returned object from the
          ``func`` is an ``ivy.Array`` or ``ivy.Container``, the default value is ``None``
          otherwise the default value is ``[[0]]``.
  
      Returns
      -------
      ret
          the function result func_ret and a dictionary of gradients of each output
          variable w.r.t each input variable.
  
      Examples
      --------
      With :class:`ivy.Array` input:
  
      >>> x = ivy.array([[1, 4, 6], [2, 6, 9]])
      >>> func = lambda x: ivy.mean(ivy.square(x))
      >>> func_ret = ivy.execute_with_gradients(func, x, retain_grads=True)
      >>> print(func_ret)
      (ivy.array(29.), ivy.array([[0.33333334, 1.33333337, 2.        ],
         [0.66666669, 2.        , 3.        ]]))
  
      With :class:`ivy.Container` input:
  
      >>> x = ivy.Container(a = ivy.array([1, 4, 6]),
      ...                   b = ivy.array([2, 6, 9]))
      >>> func = lambda x: ivy.mean(ivy.square(x))
      >>> func_ret = ivy.execute_with_gradients(func, x, retain_grads=True)
      >>> print(func_ret)
      ({
      a: ivy.array(17.666666),
      b: ivy.array(40.333332)
      },
      {
      a: {
          a: ivy.array([0.66666669, 2.66666675, 4.]),
          b: ivy.array([0., 0., 0.])
      },
      b: {
          a: ivy.array([0., 0., 0.]),
          b: ivy.array([1.33333337, 4., 6.])
      }
      })
      """
      return current_backend(None).execute_with_gradients(
          func,
          xs,
          retain_grads=retain_grads,
          xs_grad_idxs=xs_grad_idxs,
          ret_grad_idxs=ret_grad_idxs,
      )
  
imports: from typing import Sequence, Union, Optional, Tuple, Callable
  import numpy
  import itertools
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import handle_array_function, inputs_to_ivy_arrays, to_native_arrays_and_back, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 21 for calling a global function with example usage: execute_with_gradients() and returns: current_backend