code: def value_and_grad(func, argnums=0, to_numpy=False):
      """Return a function that returns func's value and gradients' values.
  
      Suitable for use in scipy.optimize with to_numpy=True.
  
      Parameters
      ----------
      func : callable
          Function whose value and gradient values
          will be computed. It must be real-valued.
      to_numpy : bool
          Determines if the outputs value and grad will be cast
          to numpy arrays. Set to "True" when using scipy.optimize.
          Optional, default: False.
  
      Returns
      -------
      func_with_grad : callable
          Function that returns func's value and
          func's gradients' values at its inputs args.
      """
      if isinstance(argnums, int):
          argnums = (argnums,)
  
      def func_with_grad(*args, **kwargs):
          """Return func's value and func's gradients' values at args.
  
          Parameters
          ----------
          args : list
              Argument to function func and its gradients.
          kwargs : dict
              Keyword arguments to function func and its gradients.
  
          Returns
          -------
          value : any
              Value of func at input arguments args.
          all_grads : list or any
              Values of func's gradients at input arguments args.
          """
          new_args = []
          for i_arg, one_arg in enumerate(args):
              if isinstance(one_arg, float):
                  one_arg = _torch.from_numpy(_np.array(one_arg))
              if isinstance(one_arg, _np.ndarray):
                  one_arg = _torch.from_numpy(one_arg)
  
              requires_grad = i_arg in argnums
              one_arg = one_arg.detach().requires_grad_(requires_grad)
              new_args.append(one_arg)
  
          value = func(*new_args, **kwargs)
          value = value.requires_grad_(True)
  
          if value.ndim > 0:
              sum_value = value.sum()
              sum_value.backward()
          else:
              value.backward()
  
          all_grads = []
          for i_arg, one_arg in enumerate(new_args):
              if i_arg in argnums:
                  all_grads.append(
                      one_arg.grad,
                  )
  
          if to_numpy:
              value = value.detach().numpy()
              all_grads = [
                  one_grad.detach().numpy() for one_grad in all_grads
              ]
  
          if len(new_args) == 1:
              return value, all_grads[0]
          return value, tuple(all_grads)
  
      return func_with_grad
  
file_path: geomstats/geomstats/_backend/pytorch/autodiff.py
imports: import functools
  import numpy
  import torch
  from torch.autograd.functional import hessian
  from torch.autograd.functional import jacobian
label_desc: Use image node_id 7 for calling a global function with example usage: value_and_grad(func, argnums, to_numpy) and returns: func_with_grad, value,  tuple, value,  all_grads