code: def grad(
      heads,
      variables,
      head_grads=None,
      retain_graph=None,
      create_graph=False,
      train_mode=True,
  ):  # pylint: disable=redefined-outer-name
      """Compute the gradients of heads w.r.t variables. Gradients will be
      returned as new NDArrays instead of stored into `variable.grad`.
      Supports recording gradient graph for computing higher order gradients.
  
      .. note::
  
        Currently only a very limited set of operators support higher order \
        gradients.
  
      Parameters
      ----------
      heads: NDArray or list of NDArray
          Output NDArray(s)
      variables: NDArray or list of NDArray
          Input variables to compute gradients for.
      head_grads: NDArray or list of NDArray or None
          Gradients with respect to heads.
      retain_graph: bool
          Whether to keep computation graph to differentiate again, instead
          of clearing history and release memory. Defaults to the same value
          as create_graph.
      create_graph: bool
          Whether to record gradient graph for computing higher order
      train_mode: bool, optional
          Whether to do backward for training or prediction.
  
      Returns
      -------
      NDArray or list of NDArray:
          Gradients with respect to variables.
  
      Examples
      --------
      >>> x = mx.nd.ones((1,))
      >>> x.attach_grad()
      >>> with mx.autograd.record():
      ...     z = mx.nd.elemwise_add(mx.nd.exp(x), x)
      >>> dx = mx.autograd.grad(z, [x], create_graph=True)
      >>> print(dx)
      [
      [ 3.71828175]
      <NDArray 1 @cpu(0)>]
      """
      head_handles, hgrad_handles = _parse_head(heads, head_grads)
  
      if isinstance(variables, NDArray):
          variables = [variables]
      else:
          assert len(variables), "variables cannot be an empty list."
      var_handles = c_handle_array(variables)
  
      retain_graph = (
          retain_graph if retain_graph is not None else create_graph
      )
      grad_vars = ctypes.POINTER(NDArrayHandle)()
      grad_stypes = ctypes.POINTER(ctypes.c_int)()
  
      check_call(
          _LIB.MXAutogradBackwardEx(
              len(head_handles),
              head_handles,
              hgrad_handles,
              len(var_handles),
              var_handles,
              ctypes.c_int(retain_graph),
              ctypes.c_int(create_graph),
              ctypes.c_int(train_mode),
              ctypes.byref(grad_vars),
              ctypes.byref(grad_stypes),
          )
      )
  
      ret = [
          _ndarray_cls(
              ctypes.cast(grad_vars[i], NDArrayHandle),
              stype=grad_stypes[i],
          )
          for i in range(len(var_handles))
      ]
      if isinstance(variables, NDArray):
          return ret[0]
      return ret
  
imports: from array import array
  from threading import Lock
  import traceback
  import ctypes
  from ctypes import c_int, c_void_p, CFUNCTYPE, POINTER, cast
  from .base import _LIB, check_call, string_types, mx_uint
  from .base import NDArrayHandle, c_array, c_handle_array, c_array_buf, MXCallbackList, SymbolHandle
  from .ndarray import NDArray, _ndarray_cls
  from .ndarray import _GRAD_REQ_MAP
  from .symbol import Symbol
  from .util import is_np_array
label_desc: Use image node_id 12 for calling a global function with example usage: grad(heads, variables, head_grads, retain_graph, create_graph, train_mode) and returns: ret, ret