code: def execute_with_gradients(
      func,
      xs: Union[tf.Tensor, tf.Variable],
      /,
      *,
      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,
          _,
      ) = _get_required_float_variables(xs, xs_grad_idxs)
  
      # Creating a tape to record operations
      with tf.GradientTape(
          persistent=True, watch_accessed_variables=False
      ) as tape:
          tape.watch(xs_required)
          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, reshape=False
      )
  
      if isinstance(y, ivy.NativeArray):
          # Gradient calculation for a single output
          grads = _set_duplicates(
              ivy.to_ivy(_grad_func(y, xs, xs_required, tape)),
              required_duplicate_index_chains,
          )
      else:
          # Gradient calculation for multiple outputs
          y = _get_native_y(y)
          grads_ = ivy.nested_map(
              lambda x: _grad_func(x, xs, xs_required, tape),
              y,
              include_derived=True,
              shallow=False,
          )
          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_)
              }
  
      # Deleting the tape if not retaining gradients
      if not retain_grads:
          del tape
  
      # 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/tensorflow/gradients.py
imports: import tensorflow
  from typing import Sequence, Union, Optional, Callable
  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