code: def grad(f, argnums=0):
      if grad.nth == 0:
          grad.f_original = f
  
      def _nth_derivative(n):
          @outputs_to_ivy_arrays
          @inputs_to_native_arrays
          def _inner(*args, **kwargs):
              max_argnum = (
                  argnums if isinstance(argnums, int) else max(argnums)
              )
              if max_argnum >= len(args):
                  raise TypeError(
                      f"differentiating with respect to {argnums=} requires at least "
                      f"{max_argnum + 1} positional arguments to be passed by the "
                      f"caller, but got only {len(args)} positional arguments."
                  )
              if isinstance(argnums, int):
                  x = args[argnums]
                  x.requires_grad_()
              elif isinstance(argnums, (tuple, list)):
                  x = []
                  for i in argnums:
                      x.append(args[i])
                      [arr.requires_grad_() for arr in x]
              else:
                  raise TypeError(
                      "argnums should be passed as int or a list/tuple of ints."
                      f" Found {type(argnums)}"
                  )
              if n == 0:
                  ret = (
                      grad.f_original(*args, **kwargs)
                      if grad.f_original is not None
                      else f(*args, **kwargs)
                  )
                  grad.nth = 0
                  return ret
              else:
                  y = _nth_derivative(n - 1)(*args, **kwargs)
  
                  # Avoid zero gradients setting requires_grads as False
                  if isinstance(y, tuple):
                      y_ones = tuple(torch.ones_like(y_) for y_ in y)
                      [
                          y_.requires_grad_()
                          for y_ in y
                          if y_.requires_grad is False
                      ]
                  elif y.requires_grad is False:
                      y.requires_grad_()
                  else:
                      y_ones = torch.ones_like(y)
  
                  dy_dx = torch.autograd.grad(
                      y,
                      x,
                      create_graph=True,
                      grad_outputs=y_ones,
                      allow_unused=True,
                  )
                  if dy_dx is None:
                      return torch.zeros_like(y)
                  return dy_dx
  
          return _inner
  
      grad.nth += 1
  
      return _nth_derivative(grad.nth)
  
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 9 for calling a global function with example usage: grad(f, argnums) and returns: _nth_derivative, _inner, ret, dy_dx, torch