code: def value_and_grad(func):
      def grad_fn(xs):
          return ivy.to_native(func(xs))
  
      def callback_fn(xs):
          y = grad_fn(xs)
  
          def autograd_fn(x):
              x = ivy.to_native(x)
              grad = torch.autograd.grad(y, x, allow_unused=True)[0]
              grad = (
                  grad
                  if grad is not None
                  else ivy.to_native(ivy.zeros_like(ivy.to_ivy(x)))
              )
              grad = ivy.to_ivy(grad)
              return grad
  
          grads = ivy.nested_map(
              autograd_fn, xs, include_derived=True, shallow=False
          )
          y = ivy.to_ivy(y)
          return y, grads
  
      return callback_fn
  
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 6 for calling a global function with example usage: value_and_grad(func) and returns: callback_fn, ivy, y,  grads, grad