code: def execute_with_gradients(
      func,
      xs: JaxArray,
      /,
      *,
      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,
          xs_required,
          required_duplicate_index_chains,
          duplicate_index_chains,
      ) = _get_required_float_variables(xs, xs_grad_idxs)
  
      func_ret = func(xs)
  
      # 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
      )
  
      if isinstance(y, ivy.NativeArray):
          # Gradient calculation for a single output
          grad_fn = jax.grad(
              lambda x: _forward_fn(
                  xs,
                  x,
                  func,
                  duplicate_index_chains,
                  xs_grad_idxs=xs_grad_idxs,
                  ret_grad_idxs=ret_grad_idxs,
              )
          )
          grads = _set_duplicates(
              grad_fn(xs_required), required_duplicate_index_chains
          )
      else:
          # Gradient calculation for multiple outputs
          grad_fn = jax.jacrev(
              lambda x: _forward_fn(
                  xs,
                  x,
                  func,
                  duplicate_index_chains,
                  xs_grad_idxs=xs_grad_idxs,
                  ret_grad_idxs=ret_grad_idxs,
              )
          )
          grads_ = grad_fn(xs_required)
          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_)
              }
  
      return _process_func_ret_and_grads(func_ret, grads, retain_grads)
  
file_path: ivy/ivy/functional/backends/jax/gradients.py
imports: import jax
  import jax.lax
  from ivy.functional.backends.jax import JaxArray, NativeArray
  from typing import Optional, Callable, Sequence, Union, Tuple
  import ivy
  from ivy.functional.ivy.gradients import _get_required_float_variables, _get_y_and_ret_idxs, _get_native_variables_and_indices, _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